Method for judging text emotion, storage medium and terminal
A text and emotional technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as long-term labor, dimension disaster, and manpower consumption, and achieve the effects of ensuring accuracy, simple process, and improving efficiency
Inactive Publication Date: 2019-07-02
SHANGHAI XIAOI ROBOT TECH CO LTD
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Problems solved by technology
Among them, in the process of training the classifier, it is necessary to manually construct a large number of feature engineering, so manual labor is required for a long time, which greatly consumes manpower; on the other hand, the text vector representation constructed in the traditional method is sparse, easy to Lead to the curse of dimensionality and make the training time of the classifier longer, thus reducing the efficiency of classifying the text to be classified
Due to the lack of semantic information representation of the text in the trained classifier, the accuracy of the result of using the classifier to classify the text to be classified is low
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[0035] As mentioned in the background technology, due to the manual construction of a large number of feature engineering, it requires manual labor for a long time, which greatly consumes manpower; on the other hand, the text vector representation constructed in the traditional method is sparse, which easily leads to dimensionality Disaster, and make the training time of the classifier longer, thus reducing the efficiency of classifying the text to be classified. Due to the lack of semantic information representation of text in the trained classifier, the accuracy of the result of using the classifier to classify the text to be classified is low.
[0036] After the technical solution of the present invention obtains the word vector of each first word in the text to be analyzed, the emotional weight of each word vector can also be determined; because the emotional weight can represent the emotion of the word vector, the word vector can represent the semantics of the first word, ...
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The invention discloses a method for judging text sentiment, a storage medium and a terminal. The method for judging the text sentiment comprises the following steps: training a word vector model by utilizing a preset original corpus; obtaining a to-be-analyzed text, wherein the to-be-analyzed text comprises a plurality of first words; converting each first word in the to-be-analyzed text into a word vector by using the word vector model; calculating to obtain a sentence vector of the to-be-analyzed text by utilizing each word vector and the corresponding emotional weight thereof, wherein theemotional weight of each word vector is predetermined; determining an emotion category of the to-be-analyzed text according to the sentence vector of the to-be-analyzed text, specifically comprising:calculating an emotion probability of the to-be-analyzed text according to the sentence vector of the to-be-analyzed text; and if the emotion probability reaches a set threshold, determining that theto-be-analyzed text is a positive emotion, or else determining that the to-be-analyzed text is a negative emotion. According to the technical scheme, the efficiency and accuracy of text sentiment analysis can be improved.
Description
technical field [0001] The invention relates to the technical field of natural language processing, in particular to a method for judging text emotion, a storage medium, and a terminal. Background technique [0002] Text sentiment analysis, also known as opinion mining (Opinion Mining), refers to the process of analyzing, processing, summarizing and inferring emotionally subjective texts. Text sentiment analysis technology plays an increasingly important role in network marketing, corporate public opinion monitoring, and government public opinion monitoring. Sentiment classification is a sub-task of text sentiment analysis technology. It uses the results of the underlying emotional information extraction to divide emotional texts into several emotional categories, for example, into two types of emotional categories, where the text corresponding to the positive emotional category is positive. Text, the text corresponding to the derogatory sentiment category is negative text....
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IPC IPC(8): G06F17/27
CPCG06F40/30G06F16/353
Inventor 陈培华朱频频
Owner SHANGHAI XIAOI ROBOT TECH CO LTD