Sentiment Analysis—An Evaluation of the Sentiment of the People: A Survey

Shah, Poojan; Swaminarayan, Priya

doi:10.1007/978-981-15-4474-3_6

Cited by 3 publications

(1 citation statement)

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“…Second, we deleted tweets with less than three words in the corpus. In general, we believe that tweets of less than three characters are not emotionally inclined or do not fully express the opinion holder’s attitude and should be removed from the corpus [ 30 ]. Third, we deleted links, usernames, topic names, and retweets from tweets in the corpus.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Zou

Xiang

2022

Entropy

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With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

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Section: Methodsmentioning

confidence: 99%