Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

Abstract

The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts is their opinions on different issues. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This work focuses on the later approach due to its various challenges and high potential. The discussions in this paper take the reader through the detailed steps of building the main two components of the lexicon-based SA approach: the lexicon and the SA tool. The experiments show that significant efforts are still needed to reach a satisfactory level of accuracy for the lexicon-based Arabic SA. Nonetheless, they do provide an interesting guide for the researchers in their on-going efforts to improve lexicon-based SA. ENDNOTES1 Some studies include the neutral case as well. 2

Sentiment classification techniques for Arabic language: A survey

“…A detailed discussion of building lexicon-based SA components is presented in [16]; the two components are a lexicon and an SA tool. The authors started by building an Arabic corpus of 4000 textual comments, collected from twitter and Yahoo!-Maktoob.…”

Sentiment Analysis for Movie Reviews Based on Four Machine Learning Techniques

“…The other approach is using a list of words, these words are related to their polarities (+1 or −1), where the model determine overall text polarity from the single polarities of words or sentences in the text. This approached is referred to as lexicon-based or unsupervised [3]. The researches about the topic of SA can be classified as document, sentence, and aspect-/feature-levels sentiment analyses.…”

A Novel Machine Learning System for Sentiment Analysis and Extraction

“…SA, on the other hand, aims to be able to divide correctly text data into categories based on the opinions the authors expressed about particular issues, using natural language. To be able to offer personalized user experiences, these two fields can be analyzed holistically [4]. The novel system proposed in this article does that by merging an auto-summarization algorithm with a sentiment analysis algorithm and examining the results using the relevant metrics.…”