Sentiment Analysis of Short Informal Text
Sentiment Analysis of Short Informal Text
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DOI：
10.1613/jair.4272
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发表时间：
2014-01-01
影响因子：
5
通讯作者：
Mohammad, Saif M.
中科院分区：
文献类型：
--
作者：
Kiritchenko, Svetlana;Zhu, Xiaodan;Mohammad, Saif M.
我们描述了一种最先进的情绪分析系统，该系统可以检测（a）简短的非正式文本消息（例如推文和短信）的情绪（消息级任务）和（b）消息中单词或短语的情绪（术语级任务）。该系统基于监督统计文本分类方法，利用各种表面形式、语义和情感特征。情感特征主要源自新颖的高覆盖率推文特定情感词典。这些词典是从带有情感词标签的推文和带有表情符号的推文自动生成的。为了充分捕捉否定语境中单词的情感，为否定词生成了单独的情感词典。该系统在 SemEval-2013 共享任务“Twitter 中的情感分析”（任务 2）中排名第一，在消息级任务中获得了 69.02 的 F 分数，在术语级任务中获得了 88.93 的 F 分数。赛后改进将 F 分数提升至 70.45（消息级任务）和 89.50（术语级任务）。该系统还在另外两个数据集上获得了最先进的性能：SemEval-2013 SMS 测试集和电影评论摘录语料库。消融实验表明，使用自动生成的词典可使性能提升高达 6.5 个绝对百分点。
We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words.The system ranked first in the SemEval-2013 shared task 'Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the- art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.