A Co-processing Method for Aspect-based Sentiment Analysis Model

• Main Authors: Yan-Wun Lee, 黎彥彣
• Other Authors: Ying-Kuei Yang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/4rnet2

碩士 === 國立臺灣科技大學 === 電機工程系 === 107 === Sentiment analysis is also being called as opinion exploration. It is a study of whether human beings are feeling positive or negative about an object or an event. It is also a subfield of natural language processing (NLP). This field actually works on some places like analyzing a product’s pros and cons or analyzing what public thought about the policy. The final purpose of sentiment analysis is to judge sentiments only by machine. The most popular methods proposed in the early stage of sentiment analysis are lexicon-based method because of the low performance of hardware at that time. We can easily run a lexicon-based method by comparing sentiment words which was already placed in dictionary and tagged by human. Today, most methods of sentiment analysis are machine-learning type methods because machines can infer beyond the limits of human beings. Nowadays, the most popular model being applied on sentiment analysis is long short-term model (LSTM). Because of the characteristic of being able to memorize previous experience, many related researches apply LSTM to be their fundamental structures. The model proposed in this thesis is also based on LSTM structure. A new aspect-based sentiment analysis method, called as multi-aspect co-processing model (MCM), is proposed in this thesis to solve the problems caused by co-processing multi-aspects. By taking out the meanings of a sentence, a sentiment representation vector can be formed by concatenating the meanings of the sentence and the meanings of its related aspect. Although the proposed model cannot work well in every occasion, it is still a valuable model in terms of solving the multi-aspect problem.