Sentence Compression for Aspect-Based Sentiment Analysis

Abstract

Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects' polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, we address this problem by proposing a framework of adding a sentiment sentence compression (Sent Comp) step before performing the aspect-based sentiment analysis. Different from the previous sentence compression model for common news sentences, Sent Comp seeks to remove the sentiment-unnecessary information for sentiment analysis, thereby compressing a complicated sentiment sentence into one that is shorter and easier to parse. We apply a discriminative conditional random field model, with certain special features, to automatically compress sentiment sentences. Using the Chinese corpora of four product domains, Sent Comp significantly improves the performance of the aspect-based sentiment analysis. The features proposed for Sent Comp, especially the potential semantic features, are useful for sentiment sentence compression.Index Terms-sentiment analysis, aspect-based sentiment analysis, sentence compression, potential semantic features.1 In this paper, we focus on Chinese sentiment analysis task. The similar method can also be used into other languages. 2 A Chinese natural language processing toolkit, Language Technology Platform (LTP) [10], was used as our dependency parser. More information about the syntactic relations can be found from their paper. The state-of-the-art graph-based dependency parsing model, in the toolkit, was trained on Chinese Dependency Treebank 1.0 (LDC2012T05).

Product Reviews Sentiment Analysis using Supervised Joint Aspect and Sentiment Model: Survey

“…Develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions. The experimental results on a review corpus of 21 popular products in eight domains demonstrate the effectiveness of the proposed approach "Wanxiang Che" [2] says, Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects' polarities.…”

A Survey on Various Features and Techniques of Text Content Classification

“…This features can involve several items such as the position of the sentence in the documents, section and the paragraph, etc, proposed the first sentence of highest ranking. The score for this features in [6] consider the first 5 sentence in the paragraph.…”

“…This feature is a similarity between sentences for each sentence S , the similarity between S and each other sentence is computed by the cosine similarity measure with a resulting value between 0 and 1 [6]. The term Weight wi and wj of term t to n term in sentences Si and Sj are represented as the vector.…”

An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data

“…However, their work only supports categorical attributes. Other effective algorithms are proposed [10][11][12][13][14][15][16][17][18][19][20][21], but all have the shortcoming that can not handle a large scale data effectively.…”