TW201416887A - Methods for sentimental analysis of news text - Google Patents

Abstract

Methods for analyzing a text are provided. The proposed method includes decomposing the text into plural sentences, each of which includes at least one clause, and the at least one clause includes at least one vocabulary, analyzing an attribute of the at least one vocabulary, wherein the attribute is selected from a group consisting of an optimistic vocabulary, a pessimistic vocabulary, a non-sentimental vocabulary and a negative adjective; accumulating each of the attributes of all the vocabularies in each of the clauses to estimate a sentimental tendency of each of the clauses, and accumulating all of the sentimental tendencies of each of the clauses counted according to each of the sentences to calculate a respective entropy value of each of the sentimental tendencies in the text to determine a sentimental tendency of the text.

Description

News text sentiment orientation analysis method

The invention relates to a method for analyzing emotional sentiment of news texts, in particular to a news text sentiment orientation analysis method using a finite state automata and an entropy value.

Relevant research on the analysis of emotional sentiment in the financial field confirms that the content of financial news often affects the stock price, trading volume and even the company's future revenue in the financial market; therefore, it has important value in practical application.

At present, among the conventional techniques for analyzing the emotional tendency of news texts, there are techniques for using machine learning techniques to automatically judge the emotional tendency of financial news as optimistic or pessimistic. However, because the technology still needs to be trained and tested by emotional language models, it is necessary to collect a considerable amount of historical data for training purposes, and must first calculate the vocabulary probability distribution of the corpus, etc., so its application is more limited. There should be room for improvement.

Therefore, how to further improve the existing technology of emotional text analysis of news texts, so that it does not need to go through emotional language model training and testing, and does not need to calculate the lexical probability distribution of corpus to improve its use efficiency, which is worthy of further discussion. issue.

As a result of the job, the inventor, in view of the lack of prior art, thought of and improved the idea of invention, and finally invented the "method of emotional analysis of news texts" in this case.

The main purpose of this case is to propose a method for analyzing emotional sentiment of news texts. This method has the vocabulary probability distribution without corpus, and uses the finite state automaton to estimate the emotional tendency of the clauses and integrates the points. The emotional tendency of the sentence, through the calculation of the entropy value, to estimate the emotional tendency of the text, etc., has the advantages of improving the efficiency of the analysis of the emotional tendency of the news text and shortening the establishment time of the related application modules of the emotional analysis method of the news text. .

Another main object of the present invention is to provide a method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary library and a finite state automata; the news The text is subjected to a sentence segmentation process to generate a plurality of sentences, wherein each sentence includes at least one clause, and each of the clauses includes at least one vocabulary; the emotional vocabulary and the negative modified vocabulary are used to the plurality of sentences And each vocabulary of each of the clauses is subjected to a lexical comparison to indicate that the vocabulary is an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negatively modified vocabulary; Converting each of the vocabulary into a representative symbol; using the finite state automaton and each of the representative symbols to estimate whether one of the emotional tendencies of the clause belongs to an optimistic, a pessimistic or a neutral; respectively, in terms of sentences After summing up the sentimental tendencies of each of the clauses contained in the sentence in the news text, it is calculated according to the sum of all the sentences of the summed sentences. One tendency of entropy (entropy) value; and The emotional tendency to determine the news text based on the isentropic value belongs to the optimism, the pessimism, or the neutrality.

A main purpose of the present invention is to provide a method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary and a finite state automata; the news The text is subjected to a sentence segmentation process to generate a plurality of sentences, wherein each sentence includes at least one clause, each of the clauses includes at least one vocabulary; and the plurality of vocabulary is used by using the negative modified vocabulary and the emotional vocabulary a lexical comparison to indicate that each vocabulary is an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negatively modified vocabulary; and each vocabulary is converted into a representative symbol according to one of the lexical comparisons And using the finite state automaton and each of the representative symbols of each of the vocabulary of the clause to estimate an emotional tendency of each of the clauses.

Another main object of the present invention is to provide a method for analyzing a text, comprising: providing a complex vocabulary library and a finite state automaton; analyzing the text to generate a plurality of sentences, each sentence comprising at least one clause having at least one vocabulary Comparing the at least one vocabulary with the plural vocabulary to indicate one of the attributes of the at least one vocabulary and one of the corresponding attributes; using the finite state automaton to compare the code to calculate each of the clauses An emotional tendency; calculating an entropy value of each of the emotional tendencies in the text by accumulating the emotional tendencies in units of the sentence; and determining an emotional state of the text according to the entropy value.

A further main object of the present invention is to provide a method for analyzing a text, comprising: disassembling the text into plural sentences, each sentence including at least one clause, and each clause includes at least one vocabulary; analyzing the at least one vocabulary An attribute, wherein the attribute is selected from the group consisting of an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary, and a negatively modified vocabulary; accumulating each of the vocabulary in each of the clauses to Calculating one of the emotional tendencies of each of the clauses; and accumulating the entropy values of each of the emotional tendencies in the text by accumulating the emotional tendencies of the clauses in each of the sentences to determine the text An emotional tendency.

The above described objects, features, and advantages of the present invention will become more apparent and understood.

The first figure is a flow chart showing a method for analyzing an emotional tendency of a news text in accordance with a preferred embodiment of the present invention. In the first figure, there is an emotional vocabulary 1, a negative modified vocabulary 2, and a finite state automaton 3. As shown in the first figure, the method for analyzing an emotional tendency of a news text includes the following steps: (S1) providing a news text; (S2) performing clause and word segmentation; (S3) converting a clause The vocabulary is an emotional symbol; (S4) determines the emotional tendency of each clause through a finite state automaton; (S5) outputs the emotional tendency of the clause; (S6) accumulates the emotional tendency of each clause in units of sentences; (S7) calculates the text Emotional entropy value; And (S8) estimate the emotional tendency of the text.

The emotional vocabulary 1 as shown in the first figure includes a plurality of optimistic vocabularies and a plurality of pessimistic words, for example:

◆Emotional Vocabulary

In addition, the negative modified vocabulary 2 as shown in the first figure includes a plurality of negative modified vocabularies, for example:

◆Negative modified vocabulary

As described above, the method for analyzing the emotional tendency of a news text according to the concept of the present invention has the following characteristics: 1. no need to establish a lexical probability distribution of the corpus; 2. a sentence-based unit, via a finite state The automaton estimates the emotional tendency of the clause; and integrates the emotional tendency of each clause, and estimates the emotional tendency of the text through the calculation of the entropy value.

In addition, as shown in the first figure, the finite state automaton 3, the decision of one of the clauses of the emotional tendency is to perform a state transition according to the following table, and the leftmost row shows the clauses. For each current state, the uppermost column shows the code for the next vocabulary, and the states in the table are the next state for each clause. Among them, S ₀ indicates that his emotional tendency is optimistic, S ₁ indicates that his emotional tendency is pessimistic, and S ₂ indicates that his emotional tendency is neutral.

1. Input symbol description: +: optimistic vocabulary; -: pessimistic vocabulary; ~: negative vocabulary; : Table is not emotional vocabulary.

2. The starting state of one of the emotional tendencies of each of the clauses is S ₂ .

3. When the final state is the S ₀ form input clause, the emotional tendency is optimistic; when the final state is the S ₁ form input clause, the emotional tendency is pessimistic; when the final state is the S ₂ form input clause, its emotional tendency Neutral.

4. The above matrix value of the finite state automaton is obtained by observing the randomly generated text.

Two examples of analyzing a news article by analyzing the emotional tendency method of the news text proposed by the present invention are listed below. The method for analyzing the emotional tendency of a news text proposed by the present invention uses an emotional news as a sample paradigm to break a corpus through a word breaking system, via the concept of the finite state automaton 3 as shown in the first figure. The operation, and finally applying the entropy value to calculate the emotional tendency of the news text is as follows:

◆ The judgment of emotional tendency can be divided into the following steps:

1. Establish a feature vocabulary database.

2. Use the "finite state automaton" to convert the lexical emotional state into the emotional state of the clause.

3 Calculate the emotional classification of each clause in each clause, and calculate the statistics of these three types by entropy values.

1. Examples of negative emotions in news texts (the following text has been processed by word breaking)

There are 4 sentences in the headline, and the number of sentences in each sentence is 1, 4, 5, and 4 respectively. For example, (Table 1): The vocabulary content of each clause is converted according to the emotional vocabulary and the negative modified vocabulary. For emotional symbols, the emotional tendency of the clauses is output through a finite state automaton, as shown in (Table 2): according to (Table 2), the emotional tendency of each clause is accumulated in units of sentences, and then the emotion of the article is estimated by entropy. The tendency is detailed as follows (Table 3):

Entropy weight calculation step

◆ Step 1: The degree of closeness d _{ij of the} emotional tendency X _ij of each clause in the normalized matrix table.

◆Step 2: Convert d _ij into probability of occurrence P _ij .

◆Step 3: Calculate the entropy value e _{j of} each criterion from P _ij .

Where m is a sentence, n is an emotional tendency, and emotional tendencies include positive (+), negative (-), and undecidable (?).

Evaluation value = (entropy ⁺ - entropy ^- ) / (entropy ⁺ + entropy ^- ) = (0.4591 ^- 0.7595) / (0.4591 + 0.7595) = -0.2465

The emotional threshold is set, and the threshold of emotional preference can be set by the user: for example, the optimistic threshold = 0.1, if the emotional tendency is 0.1, it is judged as optimistic news; for example, the pessimistic threshold = -0.1, if the emotional tendency is ≦-0.1, Then it is judged to be pessimistic news. Because the above-mentioned news text has an evaluation value of -0.2465≦-0.1, the above-mentioned news text is judged to be pessimistic, that is, it is a pessimistic news.

After the entropy value calculation, the emotional tendency of the news text can be estimated. Except for financial news, other news texts, such as political news or international news, have the same emotional sample judgment logic as the above financial news, so The method of analyzing the emotional tendency proposed by the present invention can be applied to the emotional tendency of estimating a large amount of text.

Second, the news text biased positive examples (the following text has been processed by word breaking)

The above news contains a total of 7 sentences, and the number of clauses in each sentence is 1, 3, 6, 7, 2, 2, 4, as shown in (Table 11):

The vocabulary content of each clause is converted into emotional symbols based on the emotional vocabulary and the negative modified vocabulary, and the emotional tendency of the clauses is output through the finite state automaton, as shown in (Table 12):

According to (Table 12), the emotional tendency of each clause is accumulated in units of sentences, and then the entropy of the article is estimated by entropy. The details are as follows:

Entropy weight calculation step

◆ Step 1: The degree of closeness d _{ij of the} emotional tendency X _ij of each clause in the normalized matrix table.

◆Step 2: Convert d _ij into probability of occurrence P _ij .

◆Step 3: Calculate the entropy value e _{j of} each criterion from P _ij .

Among them, m is a sentence, n is an emotional tendency, and emotional tendency includes positive (+), negative (-), and unrecognizable (?).

Evaluation value = (entropy ⁺ - entropy ^- ) / (entropy ⁺ + entropy ^- ) = (0.9010 ^- 0.4360) / (0.9010 + 0.4360) = 0.35

Because the evaluation value of the above news text is = 0.35 ≧ 0.1, the above news text is judged to be optimistic, that is, it is an optimistic news.

3. Proof of correctness of the method for analyzing emotional sentiment of news texts in accordance with the teachings of the present invention:

(A), the correct emotional tendency to distinguish

The five people manually judged the emotional news 30, using the majority decision system to determine the "correct emotional tendency", the results are as shown in (Table 21):

1. Four of the "News 3" think that the emotional tendency of this article is positive and one is negative. Judging by majority decision, this article has a correct emotional tendency to be positive.

2. Four of the "News 8" believe that the emotional tendency of this article is positive and one is negative. Judging by majority decision, this article has a correct emotional tendency to be positive.

3. One of the "News 28" believes that the emotional tendency of this article is positive and the four are negative. Judging by majority decision, this article has a correct emotional tendency to be negative.

(B), manual judgment of the correct rate

A random sample of five people judged that when the opinions were not consistent, it was judged as a manual discriminant error. The sample contained 30 news items, including "News 3", "News 8" and "News 28". The results are not consistent, so (30-3)/30=0.9, the correct rate of manual judgment can be found to be 90%, and the average time taken is 18.6 minutes.

(3), the machine judges the correct rate

The results are judged by the machine and judged with the correct emotional tendency. The results are as shown in (Table 22): From the above analysis and results, when the sample contains 30 news items, the correct rate of manual interpretation is 90%, and the average time taken is 18.6 minutes. However, using the method for analyzing the emotional tendency of the news text according to the concept of the present invention, the correct rate determined by the machine is 83.3%, and the time taken is only 5.1 seconds, so the method proposed by the present invention can be used. Verifying that the emotional tendency of the text is judged by the machine does have a relatively high accuracy rate and a relatively short time. Therefore, the emotional tendency of the news text can be estimated by the machine first by the method of analyzing the emotional tendency of the text according to the present invention. By manually verifying the correctness, the manpower and time invested can be greatly reduced, and the consistency of quality can be controlled. Therefore, the method proposed by the present invention for analyzing the emotional tendency of news texts does have its advantages.

Example:

A method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary library and a finite state automata; and performing a clause segmentation of the news text Processing to generate a plurality of sentences, wherein each sentence includes at least one clause, and each of the clauses includes at least one vocabulary; using the emotional vocabulary and the negative modified vocabulary for the plurality of sentences and their respective clauses Each vocabulary is subjected to a lexical comparison to indicate that each vocabulary is an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negatively modified vocabulary; and each vocabulary is converted according to one of the lexical comparisons a representative symbol; using the finite state automaton and each of the representative symbols to estimate that one of the clauses has an emotional tendency of belonging to an optimistic, a pessimistic or a neutral; summing the news texts in units of sentences After each of the emotional tendencies of each of the clauses included in the sentence, an entropy value of each of the emotional tendencies corresponding to all the sentences added is calculated; The emotional tendency to determine the news text based on the isentropic value belongs to the optimism, the pessimism, or the neutrality.

2. The method according to embodiment 1, wherein the determining process of the emotional tendency of each of the clauses is automatically performed by the finite state after the current state of one of the clauses is added to the next vocabulary of each of the clauses. The machine is switched to the next state; and before further adding another next vocabulary, the next state replaces the original current state and becomes the current state, so that the cycle is operated until all the clauses are The determination is completed; when the current state of each clause is the optimistic, and the next vocabulary of each clause is the optimistic vocabulary, the pessimistic vocabulary, the negative modified vocabulary or the non-emotional vocabulary, then the loyalty is added After a vocabulary, the next state of each clause becomes the optimism, the pessimism, the pessimism or the optimism; when the current state of each clause is the pessimistic, and the next vocabulary of each clause When the optimistic vocabulary, the pessimistic vocabulary, the negative modified vocabulary or the non-emotional vocabulary respectively are added to the next vocabulary, the next state of each of the clauses becomes the pessimism, the pessimism, the optimism or the When the current state of each clause is the neutral, and the next vocabulary of each clause is the optimistic vocabulary, the pessimistic vocabulary, the negative modified vocabulary or the non-emotional vocabulary, then After a vocabulary, the next state of each of the clauses becomes the optimism, the pessimism, the pessimism or the neutrality; the starting state of the emotional tendency of each clause is the neutrality, when a certain score One of the final states of the sentence is the optimistic, indicating the specific clause The emotional tendency is the optimism; when the final state of the particular clause is the pessimistic, the emotional tendency of the particular clause is the pessimistic; and when the final state of the particular clause is the neutral , indicating that the sentiment of the particular clause tends to be neutral.

3. The method according to embodiment 1 or 2, wherein the step of adding the totals in sentences further comprises the following steps:

Normalizing the frequency of occurrence of each of the emotional tendencies in the news text, and converting the normalized frequency of occurrence to a probability of occurrence p _ij ; calculating one of the entropy values of each of the emotional tendencies by p _ij

Where k=1/ln(m), i=1, 2,3,...,m,m represents the total number of one of the plural sentences, j=1,2,3,...,n,n represents One of each of these emotional tendencies; and an evaluation value = (entropy ⁺ entropy ^- ) / (entropy ⁺ + entropy ^- ) (2)

⁺ Which the entropy of entropy value is when the mood is optimistic tendency, entropy ^- entropy value of is when the mood is pessimistic tendency, when the assessment value is greater than a first threshold value, the emotional tendency of the news text is This optimism, and when the evaluation value is less than a second threshold, the sentiment of the news text tends to be pessimistic.

4. The method of embodiment 1-3, wherein the first threshold value is a positive real value and the second threshold value is a negative real value.

5. The method of embodiments 1-4, wherein the news text is one selected from the group consisting of a financial news, a political news, an international news, and a combination thereof, each of the news texts The sentence is a period Separate from other sentences, and each clause in each sentence is separated from each other by a comma or a semicolon.

6. A method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary library and a finite state automata; and performing a clause segmentation of the news text Processing to generate a plurality of sentences, wherein each sentence includes at least one clause, each of the clauses including at least one vocabulary; using the negative modified vocabulary and the emotional vocabulary to perform a lexical comparison on the plurality of sentences, Marking each vocabulary as an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negatively modified vocabulary; converting each vocabulary into a representative symbol according to one of the lexical comparisons; and using the finite state The automaton and each of the representative symbols of each of the vocabulary of the clauses are used to estimate an emotional tendency of each of the clauses.

7. The method according to embodiment 6, further comprising: calculating the entropy of each of the emotional tendencies in the news text after summing the emotional tendencies of each of the clauses included in the sentence in the news text. The value of (entropy) to determine the emotional tendency of the news text is optimistic, pessimistic or neutral.

8. A method of analyzing a text, comprising: providing a complex vocabulary library and a finite state automaton; analyzing the text to generate a plurality of sentences, each sentence comprising at least one clause having at least one vocabulary; the at least one vocabulary Comparing with the plural vocabulary to indicate one of the at least one vocabulary One of the attributes and the corresponding attribute; use the finite state automaton to compare the code to estimate the emotional tendency of each of the clauses; calculate the emotional tendency by adding the emotional tendency to each sentence, and calculate the An entropy value of an emotional tendency; and determining an emotional state of the text based on the entropy value.

9. The method of embodiment 8, wherein the text is a news vocabulary, the complex vocabulary comprising an emotional vocabulary and a negative modified vocabulary, the emotional vocabulary comprising a plurality of optimistic vocabulary and a plurality of pessimistic vocabulary The negative modified vocabulary includes a plurality of negative modified vocabulary, and each of the emotional tendencies of the text belongs to an optimistic, a pessimistic or a neutral.

10. A method of analyzing a text, comprising: disassembling the text into a plurality of sentences, each of the sentences including at least one clause, and each of the at least one clause includes at least one vocabulary; analyzing one attribute of the at least one vocabulary, wherein The attribute is selected from the group consisting of an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary, and a negatively modified vocabulary; each attribute of each vocabulary in the clause is accumulated to calculate each of the points. One of the sentences is emotionally inclined; and the entropy values of one of the emotional tendencies in the text are calculated by accumulating the emotional tendencies of the respective clauses in units of the sentence to determine an emotional tendency of the text.

In summary, the present invention provides a method for analyzing emotional sentiment of a news text, which has a vocabulary probability distribution that does not need to establish a corpus, and uses a finite state automaton to estimate the emotional tendency of the clause, in units of clauses, and Integrate each score The emotional tendency of the sentence, through the calculation of the entropy value, to estimate the emotional tendency of the text, etc., has the advantages of improving the efficiency of the analysis of the emotional tendency of the news text and shortening the establishment time of the related application modules of the emotional analysis method of the news text. Therefore, it is indeed progressive and novel.

Therefore, even though the present invention has been described in detail by the above-described embodiments, it can be modified by those skilled in the art, and is not intended to be protected as claimed.

1‧‧‧Emotional vocabulary

2‧‧‧Negative modified vocabulary

3‧‧‧Limited state automaton

First Figure: A flowchart showing a method for analyzing an emotional tendency of a news text in accordance with a preferred embodiment of the present invention.

1‧‧‧Emotional vocabulary

2‧‧‧Negative modified vocabulary

3‧‧‧Limited state automaton

Claims (10)

A method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary library and a finite state automata; and performing a sentence segmentation process on the news text, Generating a plurality of sentences, wherein each of the sentences includes at least one clause, and each of the clauses includes at least one vocabulary; using the emotional vocabulary and the negative modified vocabulary for the plurality of sentences and each of the clauses thereof The vocabulary performs a lexical comparison to indicate that each vocabulary is an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negatively modified vocabulary; and each vocabulary is converted into one according to one of the lexical comparisons a representative symbol; using the finite state automaton and each of the representative symbols to estimate that one of the clauses has an emotional tendency of belonging to an optimistic, a pessimistic or a neutral; respectively, adding the total of the news text in units of sentences After each emotional tendency of each of the clauses included in the sentence, an entropy value of each of the emotional tendencies corresponding to all the sentences added is calculated; According to these entropy values to determine the text of the news belonging to the emotional tendency is optimistic or pessimistic that the neutral. The method of claim 1, wherein the determining process of the emotional tendency of each of the clauses is from the finite state after one of the clauses is added to the next vocabulary of each of the clauses. The automaton is switched to the next state; and before further adding another next vocabulary, the next state replaces the original current state and becomes the current state State, so cyclical operation until all the clauses are judged; when the current state of each clause is optimistic, and the next vocabulary of each clause is the optimistic vocabulary, the pessimistic vocabulary When the negative modified vocabulary or the non-emotional vocabulary is added to the next vocabulary, the next state of each of the clauses becomes the optimism, the pessimism, the pessimism or the optimism; The state is the pessimistic, and when the next vocabulary of the clause is the optimistic vocabulary, the pessimistic vocabulary, the negative modified vocabulary or the non-emotional vocabulary, then the next vocabulary is added, and the clauses are respectively a state becomes the pessimism, the pessimism, the optimism or the pessimism; when the current state of each clause is the neutral, and the next vocabulary of each clause is the optimistic vocabulary, the pessimistic vocabulary, When the negative modified vocabulary or the non-emotional vocabulary is added to the next vocabulary, the next state of each of the clauses becomes the optimism, the pessimism, the pessimism or the neutrality respectively; the emotional tendency of each clause One of the beginning The state is neutral, when one of the specific clauses has a final state of the optimism, indicating that the sentiment of the particular clause tends to be the optimism; when the final state of the particular clause is the pessimistic, indicating that The sentiment of the particular clause is pessimistic; and when the final state of the particular clause is the neutral, the sentiment indicating the particular clause is the neutral. The method of claim 1, wherein the step of summing up the sentences further comprises the steps of: normalizing the frequency of occurrence of each of the emotional trends in the news text, and The frequency of occurrence after conversion is converted into a probability of occurrence pij; one of the entropy values of each of the emotional tendencies is calculated by p _ij Where k=1/ln(m), i=1, 2,3,...,m,m represents the total number of one of the plural sentences, j=1,2,3,...,n,n represents a total of one of each of the emotional tendencies; and an evaluation value = (entropy ⁺ entropy ^- ) / (entropy ⁺ + entropy ^- ) (2) where entropy + is when the sentiment tends to be optimistic The entropy value, the entropy value is the entropy value when the emotion tends to be pessimistic. When the evaluation value is greater than a first threshold, the emotional tendency of the news text is the optimism, and when the evaluation value is less than a second When the threshold is devalued, the sentiment of the news text tends to be pessimistic. The method of claim 3, wherein the first threshold is a positive real value and the second threshold is a negative real value. The method of claim 1, wherein the news text is one selected from the group consisting of a financial news, a political news, an international news, and a combination thereof, and the sentence in the news text. A sentence is separated from other sentences, and each clause in the sentence is separated from the other clauses of the sentence by a comma or a semicolon. A method for analyzing an emotional tendency of a news text, comprising: providing an emotional vocabulary library, a negative modified vocabulary library and a finite state automata; and performing a sentence segmentation process on the news text, Generating a plurality of sentences, wherein each of the sentences includes at least one clause, each of the clauses including at least one vocabulary; using the negative vocabulary The library and the emotional vocabulary perform a lexical comparison on the plurality of sentences to indicate that each vocabulary is an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary or a negative modified vocabulary; and one of the vocabulary comparison results And converting each of the words into a representative symbol; and using the finite state automaton and each of the representative symbols of each of the terms of the clause to estimate an emotional tendency of each of the clauses. The method of claim 6, further comprising: calculating one of the emotional tendencies in the news text after summing up the emotional tendency of each of the clauses included in the sentence in the news text The entropy value is used to determine the emotional tendency of the news text to be optimistic, pessimistic or neutral. A method for analyzing a text, comprising: providing a complex vocabulary library and a finite state automaton; analyzing the text to generate a plurality of sentences, each sentence comprising at least one clause having at least one vocabulary; The plurality of vocabulary libraries are compared to indicate one of the attributes of the at least one vocabulary and one of the corresponding attributes; the finite state automaton is used to compare the code to estimate an emotional tendency of each of the clauses; An entropy value of each of the emotional tendencies in the text is calculated for the unit to accumulate the emotional tendencies; and an emotional state of the text is determined according to the entropy value. The method of claim 8, wherein the text is a news article, the plural vocabulary includes an emotional vocabulary and a negative modification The vocabulary library includes a plurality of optimistic vocabulary and a plurality of pessimistic vocabulary. The negative modified vocabulary includes a plurality of negative modified vocabulary, and each of the emotional tendencies of the text belongs to an optimistic, a pessimistic or a neutral. A method for analyzing a text, comprising: disassembling the text into plural sentences, each sentence includes at least one clause, and each of the at least one clause includes at least one vocabulary; analyzing one attribute of the at least one vocabulary, wherein the attribute Or one of a group consisting of an optimistic vocabulary, a pessimistic vocabulary, a non-emotional vocabulary, and a negatively modified vocabulary; accumulating each of the vocabulary in each of the clauses to calculate each of the clauses An emotional tendency; and an entropy value of each of the emotional tendencies in the text is calculated by accumulating the emotional tendencies of the respective clauses in units of the sentence to determine an emotional tendency of the text.