中文微博情感分析研究综述_微博情感博主名字_微博情感分析工具(6)

Step 1：Extract aphrasal lexiconfrom reviews，通过规则抽取的phrasal如下图所示：

Step 2：Learn polarity of each phrase，那么，如何评价phrase的polarity呢？直观上，有这样的结论：“Positive phrases co-occur more with‘excellent’，Negative phrases co-occur more with’poor’”，这时，将问题转换成如何衡量词条之间的共现关系？于是，学者们引入了点互信息（Pointwise mutual information，PMI），它经常被用于度量两个具体事件的相关程度，公式为：

两个词条的PMI公式为：

常用的计算PMI(word1, word2)方法是分别以”word1”，”word2”和”word1 NEAR word2”为query，根据搜索引擎检索结果，得到P(word)和P(word1, word2)，如下：

P(word) = hits(word)/N

P(word1,word2) = hits(word1 NEAR word2)/N2

则有：

那么，计算一个phrase的polarity公式为（excellent和poor也可以使用其它已知极性词代替）：

Turney Algorithm在410 reviews（from Epinions）的数据集上，其中170 (41%) negative，240 (59%) positive，取得了74%的准确率（baseline为59%，均标注为positive）。

Step 3：Rate a review by the average polarity of its phrases

3. Using WordNet to learn polarity：论文见S.M. Kim and E. Hovy. 2004.Determining the sentiment of opinions. COLING 2004，M. Hu and B. Liu.Mining and summarizing customer reviews. In Proceedings of KDD, 2004.该方法步骤如下：

Create positive (“good”) and negative seed-words (“terrible”)

Find Synonyms and Antonyms

Positive Set: Add synonyms of positive words (“well”) and antonyms of negative words

Negative Set: Add synonyms of negative words (“awful”) and antonyms of positive words (”evil”)

Repeat, following chains of synonyms

Filter

以上几个方法都有较好的领域适应性和鲁棒性，基本思想可以概括为“Use seeds and semi-supervised learning to induce lexicons”，即：

Start with a seed set of words (‘good’, ‘poor’)

Find other words that have similar polarity:

Using “and” and “but”

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