WO2015053607A1 - Système et procédé d'analyse de sentiment au niveau sémantique de texte - Google Patents

Système et procédé d'analyse de sentiment au niveau sémantique de texte Download PDF

Info

Publication number
WO2015053607A1
WO2015053607A1 PCT/MY2014/000182 MY2014000182W WO2015053607A1 WO 2015053607 A1 WO2015053607 A1 WO 2015053607A1 MY 2014000182 W MY2014000182 W MY 2014000182W WO 2015053607 A1 WO2015053607 A1 WO 2015053607A1
Authority
WO
WIPO (PCT)
Prior art keywords
sentiment
graph
semantic
classification
component
Prior art date
Application number
PCT/MY2014/000182
Other languages
English (en)
Inventor
Anand Sadanandan ARUN
Weng Onn Kow
Benjamin CHU MIN XIAN
Lukose Dickson
Daniel Bahls
Original Assignee
Mimos Berhad
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mimos Berhad filed Critical Mimos Berhad
Publication of WO2015053607A1 publication Critical patent/WO2015053607A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

Definitions

  • the present invention relates to a system and method for semantic level sentiment analysis.
  • Sentiment analysis uses natural language processing which aims to determine the attitude of a speaker or writer about some topic or the overall contextual polarity of a document. Natural language processing deals with the actual text element and transforms it into a machine usable format. By using sentiment analysis, it allows a system to track products, brands and also people to determine whether they are viewed positively or negatively. Sentiment analysis is important as it allows a system to track any new product or brand perception besides tracking business reputations. It also allows individuals to get an opinion or review on something on a global scale. Recently, a lot of researches have been carried out to improve the sentiment analysis of text. An example of a system and method for sentiment analysis is disclosed in
  • United States Patent No. 2011/0137906 It involves the steps of extracting a snippet related to a subject from a content database; calculating a sentiment score for the snippet; classifying the snippet into sentiment category and classifying the snippets as positive, negative or neutral.
  • the present invention relates to a system (100) and method for semantic level sentiment analysis.
  • the system (100) comprises of a graph generator component (10) for constructing conceptual representation of a text; a sentiment dictionary (40); a sentiment taxonomy (50); and a propagation rules repository (70), wherein the system (100) is further characterised in that a semantic sentiment analyser component (20) for semantically analysing the conceptual structures and generates sentiments; a sentiment processor component (30) for receiving results from the semantic sentiment analyser component (20) and processing the received results; and a semantic sentiment patterns repository (60) which is a library of sentiment patterns stored as graph.
  • the method for semantic sentiment analysis is characterised by the steps of constructing conceptual representation of a text by a graph generator component (10); loading a terminology-box which consists of concepts and relations, concept- hierarchy and relation-hierarchy by a semantic sentiment analyser component (20); performing classification of the entire fact graph by the semantic sentiment analyser component (20); performing classification of objects involved in the fact graph by the semantic sentiment analyser component (20); performing in-graph attribute propagation graph by the semantic sentiment analyser component (20); processing the classification results graph by the semantic sentiment analyser component (20); and performing sentiment processing by a sentiment processor component (30).
  • performing classification of the entire fact graph by the semantic sentiment analyser (20) includes the steps of selecting pattern from the sentiment patterns repository (60); projecting the fact graph onto a pattern graph; extracting sentiment weight of the pattern graph; identifying the number of pattern graphs projected that the project graph can project to if no more patterns are found; and determining the classification of the entire fact graph by calculating the weighted majority vote.
  • performing classification of objects involved in the fact graph by the semantic sentiment analyser (20) includes the steps of selecting all predefined classification targets; selecting sub-graphs involving the classification targets; determining sentiment classification of the sub-graphs by calculating the weighted majority vote; repeating the step of performing classification of the entire fact graph by the semantic sentiment analyser (20); and using the results of the sentiment classification to annotate the fact graph in creating a modified fact graph.
  • performing in-graph attribute propagation graph by the semantic sentiment analyser (20) includes the steps of selecting propagation rules from a propagation rules repository (70); selecting the matching rules based on the concepts in the modified fact graph; and applying rule to assign sentiment classification to the corresponding concepts.
  • FIG. 1 illustrates a system (100) for semantic level sentiment analysis according to an embodiment of the present invention.
  • FIG. 2 (a) illustrates a flow chart of a conceptual structure generation from text according to an embodiment of the present invention.
  • FIG. 2 (b) illustrates an example of a conceptual structure generation from text.
  • FIG. 3 illustrates a flow chart of a semantic sentiment analysis process according to an embodiment of the present invention.
  • FIG. 4 (a) illustrates a flow chart of the sentiment classification of the entire fact graph according to an embodiment of the present invention.
  • FIG. 4 (b) illustrates an example of the sentiment classification of the entire fact graph.
  • FIG. 5 (a) illustrates a flow chart of the sentiment classification of objects involved in the fact graph according to an embodiment of the present invention.
  • FIG. 5 (b) illustrates an example of the sentiment classification of objects involved in the fact graph.
  • FIG. 5 (c) illustrates the result of the example from FIG. 5 (b).
  • FIG. 6 (a) illustrates a flow chart of the in-graph attribute propagation according to an embodiment of the present invention.
  • FIG. 6 (b) illustrates an example of the in-graph attribute propagation.
  • FIG. 6 (c) illustrates the result of the example from FIG. 6 (b).
  • FIG. 7 illustrates a flow chart of the sentiment processing according to an embodiment of the present invention.
  • FIG. 1 illustrates a system (100) for semantic level sentiment analysis according to an embodiment of the present invention.
  • the system (100) accepts text data as input and analyses sentiment in the text. It generates semantically valid sentiment in terms of the entire text as well as the individual entities in the text.
  • the system (100) comprises of a graph generator component (10), a semantic sentiment analyser component (20), a sentiment processor component (30), a sentiment dictionary (40), a sentiment taxonomy (50), a semantic sentiment patterns repository (60) and a propagation rules repository (70).
  • the graph generator component (10) is used to construct conceptual representation of the text.
  • the semantic sentiment analyser component (20) semantically analyses the conceptual structures and generates sentiments.
  • the sentiment processor component (30) receives the results from the semantic sentiment analyser component (20) and processes the received results.
  • the sentiment dictionary (40) comprises of an enriched list if keywords that are associated to polarity values. Each keyword is mapped to one or more sentiment categories such as positive, negative, sad, angry, happy, confident, etc. The list may be enhances with synonyms which are extracted from a lexical database.
  • the sentiment taxonomy (50) comprises of taxonomical representation of common terms to describe sentiments.
  • the semantic sentiment pattern repository (60) is a library of sentiment patterns stored as graphs. These sentiment patterns tell how sentiment terms are related to a particular noun.
  • a generated annotated semantic sentiment graph is composed of a conceptual graph query, g; a sentiment class, c e C; and a weight, w e R+. A collection of these graphs are stored in the semantic sentiment patterns repository (60).
  • the propagation rules repository (70) comprises of a set of propagation rules representing how the sentiment classes may be assigned to related concepts.
  • FIG. 2 (a) it illustrates a flow chart of the conceptual structure generation from text according to an embodiment of the present invention.
  • the graph generator component (10) accepts text as input as in step 101 before it generates conceptual representation of the text as in step 102.
  • An example of a text and its corresponding conceptual representation is shown in FIG. 2 (b). Since the text "Mary gave John a boring book" contains a set of facts, a fact graph is generated to represent the conceptual structure.
  • FIG. 3 it illustrates a flow chart of a sentiment analysis process according to an embodiment of the present invention.
  • the system (100) loads a terminology-box from the semantic sentiment analyser component (20) which comprises of concepts and relations, concept-hierarchy and relation-hierarchy as in step 210. It then selects the conceptual representation i.e. the fact graph generated in step 102 to be analysed from multiple dimensions as in step 220.
  • the semantic sentiment analyser component (20) initially performs classification of the entire fact graphs as in step 230, wherein the entire fact graph is analysed to determine the sentiment classification before it performs classification of objects involved in the fact graph as in step 240.
  • the semantic sentiment analyser component (20) performs in- graph attribute propagation as in step 250 by propagating sentiments which are associated with a particular concept to other concept that are related. Lastly, the semantic sentiment analyser component (20) sends the three classification results i.e. the sentiment classification of the entire fact graph, the sentiment classification objects involved in the fact graph and the sentiment classification by the in-graph attribute propagation to the sentiment processor component (30) for further processing as in step 260.
  • FIG. 4 (a) illustrates a flow chart of the sentiment classification of the entire fact graph according to an embodiment of the present invention which further explains step 230 of FIG. 3.
  • the fact graph is projected on to the sets of graphs in the semantic sentiment pattern repository (60) as in step 231 , wherein the sets of graphs are the sentiment patterns which are stored as graphs.
  • This is achieved through graph projection technique which results in one or more pattern graphs, p that match the fact graph as in step 232.
  • the graph projection technique measures the similarity between two graphs, wherein if a graph can be projected fully on to another graph or vice versa, it is defined as very similar. When a graph can be partially projected, it is defined as a partial match.
  • the weight of the pattern graph, w is retrieved from each of the matched pattern graph as in step 233, wherein the matched pattern graphs are the results of the graph projection technique which can either be fully projected or partially projected. If more patterns are found as in step 234, the steps are repeated from step 231 until no more patterns are found. The numbers of projections, f found are then identified for each pattern graph as in step 235. Finally, the sentiment classification of the input graph is determined using the equation below,
  • c represents the classification of the fact graph
  • w represents the weight of the pattern graph
  • f represents the number of projections or matches found as in step 236.
  • FIG. 4 (b) An example of this process is shown in FIG. 4 (b) wherein the example graph represents the sentence "Ben travelled from London to Paris in an ugly bus.”
  • a matching pattern i.e. entity->attr->ugly is found.
  • entity->attr->ugly the attribute of the concept "entity” is ugly.
  • the entire graph can be classified as negative. Entities are the objects of interest and each entity instance is described by a set of attributes that define its qualities, characteristics or properties.
  • FIG. 5 (a) illustrates a flow chart of the sentiment classification of objects involved in the fact graph according to an embodiment of the present invention.
  • the sentiment classification is performed on the sub-graphs in the fact graph where a sub-graph is composed of at least one concept.
  • a set of predefined classification target is selected as in step 241.
  • the process is conducted to determine the sentiment classification of all the entities found in the fact graph, by determining the sub-graphs that represent the classification targets as in step 243.
  • Sub-graphs are smaller graphs which are derived from the fact graph. These sub-graphs are extracted by selecting the classification target and the concepts that are directly linked to it.
  • step 234 the sentiment classification process is repeated from step 231 to step 236 until all the pattern graphs in the semantic sentiment patterns repository (60) are processed to identify the classification of that particular sub-graph.
  • results of the sentiment classification are used to annotate the fact graph in creating a modified fact graph as in step 244, wherein a modified fact graph is a fact graph that is associated with a sentiment classification.
  • FIG. 5 (b) An example of this process is shown in FIG. 5 (b).
  • the conceptual graph for the sentence "The beautiful Anna travelled in an uncomfortable and ugly bus" is used as projection.
  • entity is a classification target
  • multiple pattern graphs can be identified and used for determining the sentiment classification.
  • the results of the classification are shown in FIG. 5 (c) where it can be seen that the sentiments related to the entity Bus are negative i.e. ugly and uncomfortable whereas the one related to the entity Anna is positive i.e. beautiful.
  • FIG. 6 (a) which illustrates a flow chart of the in-graph attribute propagation according to an embodiment of the present invention, it further describes step 250 of FIG. 3.
  • sentiment classifications are applied automatically by propagation based on propagation rules which are used to perform the in-graph attribute propagation as in step 251.
  • propagation rules There are no restrictions as to what properties are to be used in the propagation rule. It can use any of the properties in the relation hierarchy. For example in FIG. 6 (b), if concepts A and B are linked together with a "hasPart" relation, then the sentiment for B is assigned to A as well.
  • the modified fact graph generated from step 244 is analysed further by applying matching rules from the propagation rules repository (70) as in step 252.
  • the rules are matched based on the concepts present in the fact graph.
  • further concepts other than the previously tagged ones can be classified as in step 253.
  • Special markers which are labels to indicate whether a particular concept is a target concept, are used to determine "Target" placeholders, wherein a target concept is a concept to which propagation rules are to be applied.
  • the system uses these special markers to decide what should be carried forward or where to find the sentiment in the match i.e. the "Sentiment Class" and to which object it should be propagated to i.e. the "target.”
  • An example of the attribute propagation is shown in FIG.
  • FIG. 6 (c) While the results of applying the above mentioned propagation rule on an annotated facts are shown in FIG. 6 (c).
  • the concept storyline is annotated as positive according to step 240. Referring now to FIG. 6 (c), it can be deducted that "Movie A” can also be classified as positive by using the propagation rule due to the "hasPart” property.
  • FIG. 7 illustrates a flow chart of the sentiment processing according to an embodiment of the present invention.
  • the sentiment processor component (30) receives the results from the semantic sentiment analyser as in step 301. It then consolidates the results to show the sentiment classification in terms of the whole sentence as well as for objects of interest in the sentence to produce the multidimensional sentiment analysis results as in step 302.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Machine Translation (AREA)

Abstract

La présente invention concerne un système et un procédé d'analyse de sentiment au niveau sémantique. Le système (100) comprend un composant de génération de graphe (10), un composant d'analyse de sentiment sémantique (20), un composant de traitement de sentiment (30), un dictionnaire de sentiments (40) une taxonomie de sentiment (50), un référentiel de modèles de sentiments sémantiques (60) et un référentiel de règles de propagation (70). Le système (100) accepte des données de texte en tant qu'entrée et analyse le sentiment dans le texte. Le procédé valide un sentiment sémantiquement valide du point de vue du texte entier ainsi que des entités individuelles dans le texte.
PCT/MY2014/000182 2013-10-10 2014-06-12 Système et procédé d'analyse de sentiment au niveau sémantique de texte WO2015053607A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
MYPI2013701910 2013-10-10
MYPI2013701910A MY167959A (en) 2013-10-10 2013-10-10 System and method for semantic-level sentiment analysis of text

Publications (1)

Publication Number Publication Date
WO2015053607A1 true WO2015053607A1 (fr) 2015-04-16

Family

ID=51690422

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/MY2014/000182 WO2015053607A1 (fr) 2013-10-10 2014-06-12 Système et procédé d'analyse de sentiment au niveau sémantique de texte

Country Status (2)

Country Link
MY (1) MY167959A (fr)
WO (1) WO2015053607A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160162582A1 (en) * 2014-12-09 2016-06-09 Moodwire, Inc. Method and system for conducting an opinion search engine and a display thereof
WO2017149443A1 (fr) * 2016-02-29 2017-09-08 Koninklijke Philips N.V. Dispositif, système et procédé de classification de biais cognitif dans des microblogues se rapportant à une preuve axée sur les soins de santé
US9875230B2 (en) 2016-04-08 2018-01-23 International Business Machines Corporation Text analysis on unstructured text to identify a high level of intensity of negative thoughts or beliefs
CN109376239A (zh) * 2018-09-29 2019-02-22 山西大学 一种用于中文微博情感分类的特定情感词典的生成方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110137906A1 (en) 2009-12-09 2011-06-09 International Business Machines, Inc. Systems and methods for detecting sentiment-based topics

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110137906A1 (en) 2009-12-09 2011-06-09 International Business Machines, Inc. Systems and methods for detecting sentiment-based topics

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
GUANG QIU ET AL: "Opinion word expansion and target extraction through double propagation", COMPUTATIONAL LINGUISTICS, vol. 37, no. 1, March 2011 (2011-03-01), pages 9 - 27, XP058000002, ISSN: 0891-2017, DOI: 10.1162/COLI_A_00034 *
LILI ZHAO ET AL: "Ontology Based Opinion Mining for Movie Reviews", 25 November 2009, KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 204 - 214, ISBN: 978-3-642-10487-9, XP019135000 *
THANH TUNG NGUYEN ET AL: "A Sentiment Analysis Model Using Ontology- enriched Conceptual Graph and Operational Rules", INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, ELECTRONICS AND ELECTRICAL TECNOLOGY - CEET 2014, 20 December 2014 (2014-12-20), XP055173074 *
VALENTINA PRESUTTI ET AL: "Sentilo: Semantic Web-based Sentiment Analysis", 21 October 2013 (2013-10-21), XP055173066, Retrieved from the Internet <URL:http://challenge.semanticweb.org/2013/submissions/swc2013_submission_9.pdf> [retrieved on 20150302] *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160162582A1 (en) * 2014-12-09 2016-06-09 Moodwire, Inc. Method and system for conducting an opinion search engine and a display thereof
WO2017149443A1 (fr) * 2016-02-29 2017-09-08 Koninklijke Philips N.V. Dispositif, système et procédé de classification de biais cognitif dans des microblogues se rapportant à une preuve axée sur les soins de santé
CN108780660A (zh) * 2016-02-29 2018-11-09 皇家飞利浦有限公司 相对于以健康护理为中心的证据对微博中的认知偏差进行分类的设备、系统和方法
US20200168343A1 (en) * 2016-02-29 2020-05-28 Koninklijke Philips N.V. Device, system, and method for classification of cognitive bias in microblogs relative to healthcare-centric evidence
CN108780660B (zh) * 2016-02-29 2023-10-20 皇家飞利浦有限公司 相对于以健康护理为中心的证据对微博中的认知偏差进行分类的设备、系统和方法
US9875230B2 (en) 2016-04-08 2018-01-23 International Business Machines Corporation Text analysis on unstructured text to identify a high level of intensity of negative thoughts or beliefs
CN109376239A (zh) * 2018-09-29 2019-02-22 山西大学 一种用于中文微博情感分类的特定情感词典的生成方法
CN109376239B (zh) * 2018-09-29 2021-07-30 山西大学 一种用于中文微博情感分类的特定情感词典的生成方法

Also Published As

Publication number Publication date
MY167959A (en) 2018-10-08

Similar Documents

Publication Publication Date Title
Bharti et al. Sarcastic sentiment detection in tweets streamed in real time: a big data approach
Unger et al. An introduction to question answering over linked data
KR101723862B1 (ko) 텍스트를 포함하는 문서 분류 및 분석 방법 및 이를 수행하는 문서 분류 및 분석 장치
CN113378565B (zh) 多源数据融合的事件分析方法、装置、设备及存储介质
KR101136007B1 (ko) 문서 감성 분석 시스템 및 그 방법
Arendarenko et al. Ontology-based information and event extraction for business intelligence
Choi et al. Identifying controversial issues and their sub-topics in news articles
US20100205198A1 (en) Search query disambiguation
US20190392035A1 (en) Information object extraction using combination of classifiers analyzing local and non-local features
CN106503192A (zh) 基于人工智能的命名实体识别方法及装置
CN104350490A (zh) 用于组织与产品相关的数据的方法、装置以及计算机可读介质
WO2015084404A1 (fr) Appariement d&#39;un document d&#39;entrée à des documents d&#39;une collection de documents
CN106294358A (zh) 一种信息的检索方法及系统
Jiménez-Zafra et al. NEGES 2019 task: negation in Spanish
WO2015053607A1 (fr) Système et procédé d&#39;analyse de sentiment au niveau sémantique de texte
CN113821588A (zh) 文本处理方法、装置、电子设备及存储介质
Belbachir et al. Using language models to improve opinion detection
Dos Reis Mota LUP: A language understanding platform
Rahul et al. Social media sentiment analysis for Malayalam
Chiriatti et al. What Makes a Review helpful? Predicting the Helpfulness of Italian TripAdvisor Reviews.
Nargis et al. Generating an emotion ontology for roman urdu text
Sharma et al. An Optimized Approach for Sarcasm Detection Using Machine Learning Classifier
JP5506482B2 (ja) 固有表現抽出装置、文字列−固有表現クラス対データベース作成装置、固有表現抽出方法、文字列−固有表現クラス対データベース作成方法、プログラム
KR101712507B1 (ko) 마이크로블로그의 개체명 링킹 시스템.
Matsumoto et al. Emotion estimation of wakamono kotoba based on distance of word emotional vector

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14783678

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14783678

Country of ref document: EP

Kind code of ref document: A1