| Summary: | 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.
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