Abstract
The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.
Highlights
With the rapid development of the Internet and social networks, an increasing amount of users begin to freely share their own opinions and comments on the web to express their personal emotional opinions on some issues or events
We propose the EBILSTM-MH for sentiment analysis on microblog to tackle these issues
One of the novelties of our method lies in the utilization of the network and multi-head attention mechanism combined with emoticons for enhancing the microblog sentiment analysis
Summary
With the rapid development of the Internet and social networks, an increasing amount of users begin to freely share their own opinions and comments on the web to express their personal emotional opinions on some issues or events. Sentiment analysis is a text classification technology that involves research fields, such as NLP, machine learning, data mining, and information retrieval. Sina Weibo, as a social network platform for sharing, disseminating, and acquiring user relationship information, has become an extremely important channel for obtaining the public’s opinions or emotions on specific events. Sentiment analysis of microblog texts has extremely high application value due to the huge user base and rich information generated by users. We collect and organize the new words appearing on Weibo in the past ten years, and add them to the user-defined dictionary of jieba word segmentation toolkit to avoid the loss of important semantic information and word segmentation errors, indirectly expand the vocabulary of Word2Vec model. The multi-head attention mechanism is used to calculate the contribution of words to global sentiment analysis, and the emotional semantic enhancement of emoticons is exerted.
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