|Table of Contents|

Sentiment analysis method for comment text(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年03期
Page:
280-285
Research Field:
Publishing date:

Info

Title:
Sentiment analysis method for comment text
Author(s):
Yang Yujuan1Yuan Huanhuan2Wang Yongli2
1.Nanjing Library,Nanjing 210018,China; 2.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
comment text sentiment analysis term frequency-inverse document frequency long short-term memory attention
PACS:
TP391
DOI:
-
Abstract:
A weighted word2vec-attention long short-term memory(WWAL)emotion analysis model is proposed to overcome the shortcomings of traditional machine learning-based sentiment analysis algorithms of relying on manual establishment of emotional dictionary and manual intervention. The role of keywords in the comment text is highlighted. Word vectors based on word2vec are formed by introducing the term frequency-inverse document frequency(TFIDF)algorithm. An attention mechanism is introduced in the long short-term memory(LSTM)networks model. Experimental results on the standard dataset show the WWAL model is better than the traditional machine learning method in terms of precision,recall and F1 indicators.

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Last Update: 2019-06-30