中文短句之情緒分類

Sentiment Classification of Short Chinese Sentences

本研究工作探索文件分類的一個極端情境，依照微網誌中具有表情符號的文字內容，來猜測該敘述的情緒類別。短句的字數有限且欠缺上下文的語境，是較傳統文件分類難處理的地方；另一方面，微網誌之短句常直接帶有一些提示情緒類別的字眼，因此有助於簡單的情緒分類。我們將微網誌的短句分為正面情緒、負面情緒兩種類別；應用詞彙與情緒標記的統計資訊，加上同義詞和情緒詞彙辭典等資訊的輔助，運用向量空間模型、向量支持機與機率模型等概念來進行分類工作。分類準確率依照實驗設計的程序不同有相當大的差異，最高約超過八成，最低則僅約五成。

We explore an extreme case of text classification. The short statements in micro-blogs were collected, and were associated by a category based on the sentiment indicated by the associated icons. We evaluated different methods that assigned the categories with just the wordings in the short statements. Short statements in micro-blogs are harder to classify because of the shortage of context, yet it is not rare for the statements to include words that may be linked to sentiments directly. In this work, we considered two polarities of sentiments: negative and positive. We employed the statistical information about the word usage, a dictionary for Chinese synonyms, and an emotional phrases dictionary to convert short statements into vectors, and applied techniques of support vector machines and probabilistic modeling for the classification task. The results of classification varied with the classification methods and experimental setups. The best one exceeded 80%, but the lowest just made 55%.