CN105512166B - A kind of traffic parallel mode of microblogging public sentiment and urban traffic situation phase mapping - Google Patents
A kind of traffic parallel mode of microblogging public sentiment and urban traffic situation phase mapping Download PDFInfo
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Abstract
The present invention provides the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping, this method comprises: acquisition obtains the Twitter message data of the traffic information theme for town in real time；To every micro-blog information of acquisition: a. carries out Formal Representation to the primitive attribute and attribute mapping of microblogging respectively；B. category attribute is set to it according to traffic subject classification data set；C. according to urban road data set, its geographical location information is extracted；Category information is fed back for traffic, extracts the specific subject fed back in microblogging；D. the index of correlation of every microblogging, including confidence level, emotion degree and different degree are calculated according to the primary attribute information of extraction.It may be implemented to carry out Fast Classification and positioning to the traffic information in microblogging through the invention, it realizes and early warning visual presentation is carried out based on internet information traffic accident, congestion, feedback category information, provide aid decision for urban traffic control, planning, emergency preplan, Resolving probiems etc..
Description
Technical field
The present invention relates to the invention belongs to internet data processing technology fields, and in particular, to a kind of microblogging public sentiment with
The traffic parallel mode of urban traffic situation phase mapping.
Background technique
Current main traffic data collection technology includes fixed sensor acquisition, Floating Car acquisition and mobile awareness
Acquisition.Fixed sensor technology utilizes the sensor node composition sensor network for being mounted on road or being laid in road again
Fixed point acquisition carried out to traffic data, but generally there are the dependence to installation site and environment, faces that initial investment is huge, life
The problems such as period is short, maintenance cost is high.Floating car technology mainly utilizes Floating Car (taxi, bicycle travelled in road network again
Deng) acquire itself GPS estimated data when driving and upload onto the server.But it is big that there is also investments, lacks other type vehicles
Data and the running data of taxi etc. may not be able to reflect really the problems such as being road conditions.With mobile Internet and movement
Intelligent handheld device is popularized, and the mobile awareness technology based on crowdsourcing is proposed therewith.Currently, both at home and abroad to basic mobile awareness
The research that technology extracts road condition data, main concentrate are positioned based on cell phone network location technology and based on end sensor again
The Real-time Traffic Information of technology extracts research.Two kinds of technologies be also respectively present network positions precision is low, frequency acquisition be not fixed with
And artificial triggering and monitoring lead to problems such as user perceive burden increase.
With the continuous development of internet, so that with on-line communities, blog, microblogging, social network sites, video sharing web sites etc.
Interaction for the Social Media rapid proliferation of representative, netizen participates in, and cyberspace is made to become gradually to become with physics " real world "
In consistent.Since the ease for use and timeliness of Social Media release information are high, so that people issue shared various information and become
It is incomparable convenient.Such as in the 2012 London Olympic Games, Transport for London office has just issued a application for being called Tube Star, it
The message issued by monitoring passenger in Twitter has got sense when a large number of users takes public transport travelling in time
By and situation, such as it is where congested in traffic, traffic accident etc. where occurs.Current network has become public reaction problem, instead
An important window for answering demand is largely gone out in traffic administration work by browsing internet it is not difficult to find that being wherein flooded with
The complaint and suggestion of existing problem.Wherein microblog is due to the spies such as its participation number is more, renewal speed is fast, user distribution is wide
Point, there are the traffic informations of a large number of users real-time release, including scene description, the evaluation of traffic congestion, the friendship to traffic accident
The problem of logical facility the various informations such as feedback.How these information are obtained in time, traffic subject classification is carried out after correct processing, and
Therefrom analysis extracts related geographical location information and calculates the index of correlation, is shown, is realized by effective method for visualizing
It is mapped from microblogging public sentiment to traffic problems, and then assists related traffic management department except traditional data acquisition method to correlation
Supplement verifying of the data such as road conditions etc. and Current traffic data acquire a problem urgently to be solved.
Summary of the invention
To solve the above-mentioned problems, the present invention provides a kind of microblogging public sentiment side parallel with the traffic of urban traffic situation phase mapping
Method, specific technical solution are as follows:
A kind of traffic parallel mode of microblogging public sentiment and urban traffic situation phase mapping, method includes the following steps:
Step 1: being risen according to the setting section city Zhong Ge title, section rank, crossroad collection, two sides building collection, section
Point latitude and longitude coordinates, road segment end latitude and longitude coordinates establish city and found section data set JRD；
Step 2: the relevant microblogging of urban traffic information theme of acquisition setting in real time, according to theme correlation and time window
Mouth range judges its validity, and microblogging effective for one is denoted as JMB；
Step 3: establishing its raw data set for each JMB, its primitive attribute is extracted, JMB_ is denoted as
original；
Step 4: carrying out processing extraction to raw data set, the property set that mapping is formed with raw data set, note are established
Make: JMB_processed；
Step 5: self defined time window, establishes the traffic subject dataset JCD in self defined time window；Wherein thing
Therefore class data set is denoted as Set_accident, congestion class data set is denoted as Set_jam, and feedback sort data set is denoted as Set_
feedback；For every micro-blog information JMB, according to its affiliated section self defined time corresponding with the addition of traffic subject categories
In data set in window；For Set_accident and Set_feedback, real time information dynamic updates；For Set_jam,
The congestion index in section belonging to updating；
Step 6: to part attribute information and updated three classes traffic subject dataset in JMB_processed
Set_accident, Set_jam and Set_feedback, according to the starting point of relevant road segments in urban road data set JRD
Latitude and longitude coordinates value and location information carry out real-time visual in different ways respectively in map and show.
Further, in step 3, JMB is expressed using vector form, specific as follows:
JMB_original=(Publisher, PTime, Content, Ptemi, Cnt_forward, Cnt_comment,
Cnt_like, Cnt_pic, Plocation, Emoticon), wherein
Publisher is the publisher of this microblogging, while can acquire association attributes, including account type Publisher_
Type, bloger's title Publisher_name, bloger location Publisher_city, bloger's number of fans Publisher_
fanscnt；
T_publish is this microblogging issuing time；
Content is this microblogging content of text；
Ptemi is this microblogging issue client terminal；
Cnt_forward is this microblogging forwarding number, and Cnt_forward >=0；
Cnt_comment is that this microblogging comments on number, and Cnt_comment >=0；
Cnt_like is that this microblogging thumbs up number, and Cnt_like >=0；
The picture number that Cnt_pic includes for this microblogging, and Cnt_pic >=0；
Plocation is the publication place that this microblogging includes；
Emoticon is the emoticon that this microblogging includes；
Further, in step 4, JMB_processed to embody form as follows:
JMB_processed=(Category, Road_name, Road_type, Location_name, Location_
Type, Index_emotion, Target, Index_reliability, Index_influence), wherein
Category is the affiliated traffic subject categories of this microblogging JMB, and value, which is in traffic classification data set, includes
Classification, value range is [traffic accident, traffic congestion and traffic are fed back]；
Road_name is the city road title that this microblogging includes, and value range is corresponding urban traffic road data
The road of concentration；
Road_type is the city road title that this microblogging includes, and value range is corresponding urban traffic road data
The road grade of concentration；
Location_name is the particular geographic location title that this microblogging includes；
Location_type is the particular geographic location classification that this microblogging includes, and value range represents intersection for 1
Mouthful, 2 represent the building by road；
Target is the traffic theme of traffic feedback sort microblogging feedback, and value range is [signal lamp, traffic marking, traffic
Mark, traffic guardrail, monitoring device]；
Index_emotion is the emotion degree score of this microblogging, and value range is set of integers Z；
Index_reliability is the confidence score of this microblogging, and ity >=0 Index_reliabil；
Index_importance is the different degree score of this microblogging, and Index_importance >=0.
Further, in step 4, the attribute information of JMB_processed obtains as follows:
S1 determines classification: for the effective microblogging JMB acquired in real time, carrying out nature language according to its content of text
The urban transportation subject classification data set JCD of phrase and building after speech participle determines the traffic subject categories belonging to it, if
Determine the Category in JMB_processed.
S2, geography information extract: for every micro-blog information JMB, according in its primary attribute set JMB_original
Relevant field and building urban road data set JRD, extract its geographical location information, including affiliated section, intersection
Deng Road_name, Road_type, Location_name, Location_type in setting JMB_processed；Simultaneously
It is extracted to feedback sort microblogging and feeds back theme Target；
S3, index calculate: for every micro-blog information JMB, according to the phase in its primary attribute set JMB_original
It closes field and associated data set calculates its Intrusion Index, set Index_emotion, Index_ in JMB_processed
Reliability and Index_importance, i.e. emotion degree, confidence level and different degree.
Further, it in step S1, specifically carries out as follows:
1) urban transportation theme micro-blog information is acquired, artificial screening classifies traffic accident, traffic congestion, traffic feedback respectively
500, construct traffic classification corpus TCM；
2) using the Based on Class Feature Word Quadric under corpus TCM tri- classification of ZSCORE algorithm extraction and to belonging to each word of calculating
Category score；
3) score according to each word in JMB_originalWordList under affiliated three classifications calculates entire word
Score of the group under three classification, acquirement point are highest as its affiliated classification；If being scored at 0, it is labeled as and traffic information
Unrelated rubbish microblogging, is no longer further processed.
Further, it in step S2, specifically carries out as follows:
1) if the releasing position Plocation of JMB_original label is not empty, and it includes the section names in JRD
Claim, then mark the Road_name and Road_type in JMB_processed, go to step c, otherwise in next step；
2) WordList in JMB_original is scanned, if setting is marked comprising the road section information in JRD
Remember the Road_name and Road_type in JMB_processed；If branch separates setting institute comprising a plurality of road section information
Belong to section；If comprising affiliated section be not more than 2, go to step c；
3) according to the content of text Content in JMB_original judge its whether include belonging to road crossroad information or
Building information, if any the Location_name and Location_type then marked in JMB_processed.
Further, in the S3 step, the Index_emotion in the JMB_processed of JMB, Index_ are calculated
Reliability and Index_importance method difference is as described below:
1) emotion vocabulary and emoticon emotion Score Lists are read, in JMB_original WordList and
Emoticon is scanned respectively, to comprising emotion word and emoticon score add up, calculate mood degree score, setting
Index_emotion in JMB_processed；Index_emotion is greater than 0, then it represents that it is positive emotion, Index_
Emotion is equal to 0, then does not include obvious emotion, and Index_emotion includes negative emotion less than 0；
2) according to publisher's information Publisher relevant information in JMB_original, packet knows account type, Bo Zhuming
Title, bloger location, bloger's number of fans, judge publisher whether in history publisher's data set, it is as credible in read its if
Spend score, as nothing if according to associated information calculation its initial trusted degree score.Its score can also be in system use process by reality
Border user dynamically adjusts after manually marking.Then further according to the factors dynamic weighting such as this picture number, issue source, releasing position
The confidence score of this microblogging is calculated, the Index_reliability in JMB_processed is set；
3) according to the publisher's information Publisher relevant information and forwarding number, comment number, point in JMB_original
Number, picture number are praised, while according to the historical weibo information of acquisition in nearly ten days, being calculated using similarity algorithm with theme score, being adopted
With its different degree score of the mode COMPREHENSIVE CALCULATING of weighting, the Index_importance in JMB_processed is set.
Further, it in step 6, specifically carries out as follows:
1) combine city map, to updated three classes traffic subject dataset Set_accident, Set_jam and
Set_feedback is right according to the starting point latitude and longitude coordinates value and location information of relevant road segments in urban road data set JRD
Color rendering is carried out to affiliated section according to its updated congestion index in congestion category information, is had a good transport and communication network with indicating different
Degree/congestion level；
2) to traffic shape accident and feedback category information, different icon dynamic and visuals is respectively adopted and shows；
3) simultaneously in the specifying information of pop-up, then with different icons mark respectively the emotion degree of JMB, confidence level with again
It spends, to achieve the purpose that traffic public sentiment is mapped to urban traffic situation.
The traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping provided by the present invention has following excellent
Point:
The present invention, according to its affiliated traffic information classification and affiliated section, is added corresponding by handling micro-blog information
Data set, which calculates each section according to the micro-blog information in time window, updates its road conditions index；Different traffic classifications are believed
Breath carries out visualization display by road segment colors rendering and different icons in open map tool, realizes microblogging public sentiment and city
The mapping of city's road conditions.It may be implemented to carry out Fast Classification and positioning to the traffic information in microblogging through the invention, realization is based on
Internet information traffic accident, congestion, feedback category information carry out early warning visual presentation, are urban traffic control, planning, emergency
Prediction scheme, Resolving probiems etc. provide aid decision.
Detailed description of the invention
Fig. 1 is specific implementation process flow chart of the present invention；
Certain former such as micro-blog information of acquisition in Fig. 2 in the embodiment of the present invention 1；
Fig. 3 is that the map example that the embodiment of the present invention 1 generates is shown.
Specific embodiment
With reference to the accompanying drawing and the embodiment of the present invention is to a kind of microblogging public sentiment and urban traffic situation phase mapping of the invention
Traffic parallel mode is described in further detail.
Embodiment 1:
To make this those skilled in the art more fully understand implementation process of the present invention, below with the act of Qingdao Urban Area road network
Example is described in further detail embodiment.
Fig. 1 is specific implementation process flow chart of the present invention, our implementation process is carried out by the process of Fig. 1.
Step 101, microblog data is passed through with the predefined traffic associative key such as " Qingdao traffic ", " Qingdao Mount East Road "
Capture program grabs the relevant microblogging of Qingdao Urban Area traffic information theme and its correlation attribute value in Sina weibo platform in real time, packet
It includes the relevant information of publisher and microblogging text, thumbs up number, comment number, forwarding number etc., for a freshly harvested microblogging.
The microblogging raw information that grabs as shown in Fig. 2,
Due to its content include traffic subject key words, while its issuing time whether in the time window of setting (with
18 points of October 7 in 2015 collects the time of this microblogging as current system, to be effective apart from current time 60 hours
Time window then judges that it, for effective microblogging, is denoted as JMB；
Step 102, Formal Representation JMB extracts its primary attribute, expresses its primitive attribute collection using vector form,
It is denoted as:
JMB_original=(Publisher, PTime, Content, WordList, Ptemi, Cnt_forward,
Cnt_comment, Cnt_like, Cnt_pic, Plocation, Emoticon) wherein
Account type Publisher_type=" regular account ", the title Publisher_name=of Publisher is " perhaps
Promise _ ruby ", location Publisher_city=" Qingdao ", number of fans Publisher_fanscnt=83；
T_publish=" 09:46 on the 12nd of August in 2015 "；
Content=" I had never expected that completely traffic congestion is so severe on this Shandong road absolutely, be really in the pot~Shandong north of a road to
The each crossing in south is all very stifled, and to walk Shandong road please think carefully the Qingdao@Traffic Announcement FM897@Qingdao traffic police "；
Ptemi=" iPhone 6 "；
Cnt_forward=1；
Cnt_comment=1；
Cnt_like=0；
Cnt_pic=1；
Plocation=null；
Emoticon=[[declining] [declining] [declining]]；
Step 103, determine classification: for JMB, natural language processing being used to the Content in its JMB_original
Tool combines customized traffic dictionary to be segmented, and forms effective phrase WordList=(absolutely；It I had never expected；Shan Donglu；Entirely
Line；Traffic congestion；So；It is severe；Really it is；It is in the pot；Shan Donglu；North orientation south；It is each；Crossing；It is very stifled；Shan Donglu；Think carefully；Qingdao traffic
Broadcast FM897；Qingdao traffic police), phrase feeds back the phrase score difference under three classification in traffic accident, traffic congestion, traffic
It calculates in the following ways, CategoryScore is the score of entire phrase, and WordScore is the score of single word:
Wherein n is the word number in WordList.Due to CategoryScore three classification under score be respectively [8,
27,2], therefore the Category=" traffic congestion " in JMB_processed is set.
Step 104, geography information extracts: for JMB, since its Plocation is sky, i.e., publisher is not demarcated at that time
Position, then use to content participle after phrase WordList in include Qingdao road data collection JRD in section title,
Road_name=" Shan Donglu ", the Road_type=1 in JMB_processed are set, due to not comprising specific location,
Location_name and Location_type is sky.Since it is congestion category information, feedback theme Target is also empty；
Step 105, index calculates: for JMB, with EmotionWords indicate it includes emotion word set, scanning
Emotion word score Score_words=-5 in WordList, EmotionWords.Its emoticon emotion is scored at Score_
Emoticon=-3, then
Index_emotion=Score_words+Score_emoticon
The Index_emotion=-8 in JMB_processed is set, i.e. the emotion of JMB is negative emotion.
Since publisher's " promise _ ruby " of JMB is in history publisher's data set, and according to the traffic that it is issued
The micro-blog information etc. of theme has calculated its confidence score, directly reads its publisher's confidence score Score_ here
Publisher=4.4 points (value range 0-5).The initial trusted degree score of publisher can be calculated according to following methods:
Wherein, α _ i indicates that the weight of i-th of factor, X_i are i-th of influence factor, and k is the number of influence factor.
Here the factor considered includes: the location account type whether consistent with setting city, publisher of publisher, publisher
Number of fans issues for each person monthly historical traffic microblogging item number, the number by system user labeled as the not firm disappearance of publication.Then
1 point is obtained further according to each picture, most 3 points, then its picture number score Score_cntpic=1；According to using mobile phone objective
Family end issues to obtain 1 point, and otherwise 0 point, then issue source score Score_ptemi=1；2 points are obtained according to releasing position is identified, otherwise
0 point, then releasing position score Score_plocation=0.So JMB obtains confidence score are as follows:
Index_reliability=Score_publisher+Score_cntpic+Score_pte mi+Score_
plocation
Set the Index_reliability=6.4 in JMB_processed.
According in JMB_original publisher's information Publisher relevant information and forwarding number, comment number, thumb up
Number, picture number, while according to the historical weibo information of acquisition in nearly ten days, it is calculated using similarity algorithm with theme score, is used
Its different degree score of the mode COMPREHENSIVE CALCULATING of weighting sets the Index_importance in JMB_processed.
Index_importance=Score_forward+Score_comment+Score_like+ Score_same
Step 106, section updates: since JMB belongs to traffic congestion category information, being then added in self defined time window
Under congestion class Set_jam in " Shan Donglu " data set.In data set, wherein accident class data set is denoted as Set_accident, gathers around
Stifled class data set is denoted as Set_jam, and feedback sort data set is denoted as Set_feedback.For Set_accident and Set_
Feedback, real time information dynamic update；For Set_jam, the congestion index in affiliated section is updated；
Step 107, map visualization: the display of microblogging public sentiment map visualization, which uses, uses Baidu map API, after update
Set_jam, according to the starting point latitude and longitude coordinates value of the East Zhongshan Road urban road data set JRD, updated congestion refers to
Number (the microblogging public sentiment item number in time window) carries out the rendering of reddish yellow green color to affiliated section according to customized threshold interval,
To indicate different degree of having a good transport and communication network/congestion levels.It is marked respectively in the specifying information of pop-up, then with different icons simultaneously
The index of correlation (emotion degree, confidence level and different degree) of JMB realizes the parallel mapping of traffic public sentiment to urban traffic situation.
Microblogging public sentiment is set each other off with urban traffic situation when Qingdao Urban Area main roads on October 7th, 2,015 18 is shown in Fig. 3
The Baidu map for the traffic parallel mode building penetrated, self defined time window are 60 hours, and congestion uses color rendering,
This figure does not show that the microblogging public sentiment JMB_processed information in figure is shown as randomly selecting, traffic accident, traffic feedback
Situation is directly shown in map.From figure 3, it can be seen that microblogging public sentiment and urban traffic situation may be implemented in method of the invention
Mapping may be implemented to carry out Fast Classification and positioning to the traffic information in microblogging through the invention, realize and be believed based on internet
Traffic accident, congestion, feedback category information progress early warning visual presentation are ceased, for urban traffic control, planning, emergency preplan, is asked
The key to exercises certainly waits and provides aid decision.
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical scheme and beneficial effects
It describes in detail bright, it should be understood that the above is only a specific embodiment of the present invention, is not intended to restrict the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution, improvement and etc. done should be included in guarantor of the invention
Within the scope of shield.
Claims (6)
1. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping, which is characterized in that this method includes following step
It is rapid:
Step 1: being passed through according to the setting section city Zhong Ge title, section rank, crossroad collection, two sides building collection, section starting point
Latitude coordinate, road segment end latitude and longitude coordinates establish city road data set JRD；
Step 2: the relevant microblogging of urban traffic information theme of acquisition setting in real time, according to theme correlation and time window model
It encloses and judges its validity, microblogging effective for one is denoted as JMB；
Step 3: establishing its raw data set for each JMB, its primitive attribute is extracted, JMB_original is denoted as；
Step 4: carrying out processing extraction to raw data set, the property set for forming mapping with raw data set is established, is denoted as:
JMB_processed；
Step 5: self defined time window, establishes the traffic subject dataset JCD in self defined time window；Wherein accident class
Data set is denoted as Set_accident, and congestion class data set is denoted as Set_jam, and feedback sort data set is denoted as Set_feedback；
For every micro-blog information JMB, it is added in corresponding self defined time window according to its affiliated section with traffic subject categories
In data set；For Set_accident and Set_feedback, real time information dynamic updates；For Set_jam, belonging to update
The congestion index in section；
Step 6: to part attribute information and updated three classes traffic subject dataset Set_ in JMB_processed
Accident, Set_jam and Set_feedback, according to the starting point longitude and latitude of relevant road segments in urban road data set JRD
Degree coordinate value and location information carry out real-time visual in different ways respectively in map and show；
In step 4, JMB_processed to embody form as follows:
JMB_processed=(Category, Road_name, Road_type, Location_name, Locat ion_
Type, Index_emotion, Target, Index_reliability, Index_importance), wherein
Category is the affiliated traffic subject categories of this microblogging JMB, and value is the class for including in traffic classification data set
Not, value range is [traffic accident, traffic congestion and traffic are fed back]；
Road_name is the city road title that this microblogging includes, and value range is in corresponding urban traffic road data set
Road；
Road_type is the city road title that this microblogging includes, and value range is in corresponding urban traffic road data set
Road grade；
Location_name is the particular geographic location title that this microblogging includes；Location_type is that this microblogging includes
Particular geographic location classification, value range is 1 to represent intersection, and 2 represent the building by road；
Target be traffic feedback sort microblogging feedback traffic theme, value range be [signal lamp, traffic marking, traffic sign,
Traffic guardrail, monitoring device]；
Index_emotion is the emotion degree score of this microblogging, and value range is set of integers Z；
Index_reliability is the confidence score of this microblogging, and Index_reliability >=0；
Index_importance is the different degree score of this microblogging, and Index_importance >=0；
In step 4, the attribute information of JMB_processed obtains as follows:
S1 determines classification: for the effective microblogging JMB acquired in real time, carrying out natural language point according to its content of text
The urban transportation subject classification data set JCD of phrase and building after word determines the traffic subject categories belonging to it, setting
Category in JMB_processed；
S2, geography information extract: for every micro-blog information JMB, according to the phase in its primary attribute set JMB_ori ginal
The urban road data set JRD for closing field and building, extracts its geographical location information, including affiliated section, intersection, sets
Road_name in JMB_processed, Road_type, Locati on_name, Location_type；Simultaneously to feedback
Class microblogging extracts it and feeds back theme Target；
S3, index calculate: for every micro-blog information JMB, according to the related words in its primary attribute set JMB_original
Section and associated data set calculate its Intrusion Index, set Index_emotion, Index_ in JMB_processed
Reliability and Index_importance, i.e. emotion degree, confidence level and different degree.
2. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping according to claim 1, feature exist
In, in step 3, JMB is expressed using vector form, specific as follows:
JMB_original=(Publisher, PTime, Content, WordList, Ptemi, Cnt_forwa rd, Cnt_
Comment, Cnt_like, Cnt_pic, Plocation, Emoticon), wherein
Publisher is the publisher of this microblogging, while can acquire association attributes, including account type Publisher_
Type, bloger's title Publisher_name, bloger location Publisher_city, bloger's number of fans Publisher_
fanscnt；
PTime is this microblogging issuing time；
Content is this microblogging content of text；
WordList is the phrase carried out after natural language participle to this microblogging content of text；
Ptemi is this microblogging issue client terminal；
Cnt_forward is this microblogging forwarding number, and Cnt_forward >=0；Cnt_comment is that this microblogging comments on number,
And Cnt_comment >=0；
Cnt_like is that this microblogging thumbs up number, and Cnt_like >=0；
The picture number that Cnt_pic includes for this microblogging, and Cnt_pic >=0；
Plocation is the publication place that this microblogging includes；
Emoticon is the emoticon that this microblogging includes.
3. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping according to claim 1, feature exist
In, in step S1, specifically progress as follows:
1) urban transportation theme micro-blog information is acquired, artificial screening classification traffic accident, traffic congestion, traffic feed back each 500,
Construct traffic classification corpus TCM；
2) using the Based on Class Feature Word Quadric under corpus TCM tri- classification of ZSCORE algorithm extraction and to each word generic of calculating
Score；
3) score according to each word in the WordList of JMB_original under affiliated three classifications calculates entire phrase
Score under three classification, acquirement point are highest as its affiliated classification；If being scored at 0, labeled as with traffic information without
The rubbish microblogging of pass, is no longer further processed.
4. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping according to claim 1, feature exist
In, in step S2, specifically progress as follows:
If 1) the releasing position Plocation of JMB_original label is not empty, and it includes the section title in JRD,
The Road_name and Road_type in JMB_processed are then marked, step c is gone to, otherwise in next step；
2) WordList in JMB_original is scanned, if setting is marked comprising the road section information in JRD
Road_name and Road_type in JMB_processed；If comprising a plurality of road section information, branch is separated belonging to setting
Section；If comprising affiliated section be not more than 2, go to step c；
3) judge whether it includes affiliated road crossroad information or building according to the content of text Content in JMB_original
Object information, if any the Location_name and Locati on_type then marked in JMB_processed.
5. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping according to claim 1, feature exist
In, in the S3 step, calculate the Index_emotion in the JMB_processed of JMB, Index_reliability with
Index_importance method difference is as described below:
1) emotion vocabulary and emoticon emotion Score Lists are read, to the WordList and Emoticon in JMB_original
Be scanned respectively, to comprising emotion word and emoticon score add up, calculate mood degree score, set JMB_
Index_emotion in processed；Index_emotion is greater than 0, then it represents that it is positive emotion, Index_
Emotion is equal to 0, then does not include obvious emotion, and Index_emot ion includes negative emotion less than 0；
2) according to publisher's information Publisher relevant information in JMB_original, including account type, bloger's title,
Bloger location, bloger's number of fans judge that publisher whether in history publisher's data set, obtains as read its confidence level if
Point, as according to associated information calculation, its initial trusted degree score, score can also be in system use processes by actually using without if
Family dynamically adjusts after manually marking, and then calculates this further according to this picture number, issue source, releasing position factor dynamic weighting
The confidence score of microblogging sets the Index_reliability in JMB_processed；
3) according in JMB_original publisher's information Publisher relevant information and forwarding number, comment number, thumb up
Number, picture number, while according to the historical weibo information of acquisition in nearly ten days, it is calculated using similarity algorithm with theme score, is used
Its different degree score of the mode COMPREHENSIVE CALCULATING of weighting sets the Index_importance in JMB_proc essed.
6. the traffic parallel mode of a kind of microblogging public sentiment and urban traffic situation phase mapping according to claim 1, feature exist
In, in step 6, specifically progress as follows:
1) city map is combined, to updated three classes traffic subject dataset Set_accident, Set_j am and Set_
Feedback, according to the starting point latitude and longitude coordinates value and location information of relevant road segments in urban road data set JRD, for gathering around
Stifled category information carries out color rendering to affiliated section according to its updated congestion index, with indicate different degree of having a good transport and communication network or
Congestion level；
2) to traffic shape accident and feedback category information, different icon dynamic and visuals is respectively adopted and shows；
3) simultaneously in the specifying information of pop-up, then with different icons mark respectively the emotion degree of JMB, confidence level with it is important
Degree, to achieve the purpose that traffic public sentiment is mapped to urban traffic situation.
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| CN108038102B (en) * | 2017-12-08 | 2021-05-04 | 北京小米移动软件有限公司 | Method and device for recommending expression image, terminal and storage medium |
| CN108710654B (en) * | 2018-05-10 | 2021-03-26 | 新华智云科技有限公司 | Public opinion data visualization method and equipment |
| CN109471936A (en) * | 2018-10-11 | 2019-03-15 | 上海叔本华智能科技有限公司 | A method and system for feature classification of equipment maintenance information |
| CN110134876B (en) * | 2019-01-29 | 2021-10-26 | 国家计算机网络与信息安全管理中心 | Network space population event sensing and detecting method based on crowd sensing sensor |
| CN110260874A (en) * | 2019-06-19 | 2019-09-20 | 广州交投机电工程有限公司 | A kind of publication of information and feedback method and system |
| CN110428625A (en) * | 2019-08-12 | 2019-11-08 | 安徽赛福贝特信息技术有限公司 | A kind of wisdom traffic big data acquisition early warning system |
| CN110781393A (en) * | 2019-10-23 | 2020-02-11 | 中南大学 | Element extraction algorithm of traffic events based on graph model and dilated convolutional neural network |
| CN110910637A (en) * | 2019-11-19 | 2020-03-24 | 上海易点时空网络有限公司 | Content evaluation method, device and equipment based on traffic violation |
| CN111552772A (en) * | 2020-04-22 | 2020-08-18 | 中国计量大学 | Real-time traffic road condition text data and traffic volume combined visual analysis method |
| CN112749254A (en) * | 2020-12-30 | 2021-05-04 | 北京航空航天大学 | Traffic anomaly cause analysis method based on microblog data |
| CN112949305B (en) * | 2021-05-13 | 2021-08-13 | 平安科技（深圳）有限公司 | Negative feedback information acquisition method, device, equipment and storage medium |
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