基于词典和弱标注信息的电影评论情感分析

针对评论文本情感分析研究中数据标注费时费力的问题，提出了一种新的数据自动标注方法。首先，通过基于情感词典的方法计算出评论文本的情感倾向；其次，利用用户评分的弱标注信息和基于词典方法的情感倾向对评论文本自动标注；最后，利用支持向量机（SVM）对评论文本进行情感分类。所提出的数据自动标注方法在两种类型数据集情感分类准确率上分别达到了77.2%和77.8%，相对于单一的利用用户评分对数据标注的方法，分别提高了1.7个百分点和2.1个百分点。实验结果表明，提出的数据自动标注方法在电影评论情感分析中能提高分类效果。

Sentiment analysis of movie reviews based on dictionary and weak tagging information

Focused on the time-consuming and laborious problem of data annotation in review text sentiment analysis, a new automatic data annotation method was proposed. Firstly, the sentiment tendency of the review text was calculated based on the sentiment dictionary. Secondly, the review text was automatically annotated by using the weak tagging information of the user and the sentiment tendency based on the dictionary. Finally, Support Vector Machine (SVM) was used to classify the sentiment of the review text. The proposed method reached 77.2% and 77.8% respectively in the accuracy of sentiment classification on two types of data sets, which were 1.7 percentage points and 2.1 percentage points respectively higher than those of the method only based on user rating. The experimental results show that the proposed method can improve the classification effect in movie reviews sentiment analysis.