領域相關詞彙極性分析及文件情緒分類之研究

Domain Dependent Word Polarity Analysis for Sentiment Classification

游和正(Ho-Cheng Yu)

指導教授 ： 陳信希

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2012年)

https://doi.org/10.6342/NTU.2012.01341

摘要

情緒分析(Sentiment Analysis)主要探討文字撰寫者所表示出來的情緒狀態，分析的結果具有高度領域相關(Domain Dependent)的特性，針對不同領域的文章進行分析，可能會有不同的結果。因此，本論文使用三種完全不同領域的語料進行研究，探討不同領域的詞彙所表現出的情感程度，並依照探討的結果提出不同權重的計算方式，反映各領域詞彙的情感程度的變化，再採用監督式機器學習SVM方法進行分類，實驗結果顯示能有效改善在各領域中的意見分類F1效能。

關鍵字

情緒分析 ； 機器學習 ； SVM

並列摘要

The researches of sentiment analysis aim at exploring the emotional state of writers. The analysis highly depends on the application domains. Analyzing sentiments of the articles in different domains may have different results. In this study, we focus on corpora from three different domains, then examine the polarity degrees of vocabularies in these three domains, and propose methods to capture sentiment differences. Finally, we apply the results to sentiment classification with supervised SVM learning. The experiments show that the proposed methods can effectively improve the sentiment classification performance.

並列關鍵字

Sentiment Analysis ； Machine Learning ； SVM

參考文獻

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