Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter

Abstract

This paper describes our sentiment analysis systems which have been built for SemEval-2015 Task 10 Subtask B and E. For subtask B, a Logistic Regression classifier has been trained after extracting several groups of features including lexical, syntactic, lexiconbased, Z score and semantic features. A weighting schema has been adapted for positive and negative labels in order to take into account the unbalanced distribution of tweets between the positive and negative classes. This system is ranked third over 40 participants, it achieves average F1 64.27 on Twitter data set 2015 just 0.57% less than the first system. We also present our participation in Subtask E in which our system has got the second rank with Kendall metric but the first one with Spearman for ranking twitter terms according to their association with the positive sentiment.

SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter

“…For example, (Mohammad et al, 2013) used SVM model with several types of features including terms, POS and sentiment lexicons in Twitter data set. (Hamdan et al, 2015a;Hamdan et al, 2015c;Hamdan et al, 2015b) have also proved the importance of feature extraction with logistic regression classifier in Twitter and reviews of restaurants and laptops. They extracted terms, sentiment lexicon and some semantic features like topics.…”

Lexicon based Acronyms and Emoticons Classification of Sentiment Analysis (SA) on Big Data

“…Therefore, unwanted data is removed from the data set because, the meaningless data are useless in nature [13,14]. The data collected and features are analyzed and selected for using methods such as unigram and n-gram for tokenizing the sentiment word to enrich the data quality [15].Therefore, the data is pre-processed in an effective manner. The preprocessing algorithm is shown in figure. 2.…”

“…The lexical or lexicon based approach is a method for teaching dictionary based approach described by Michael Lewis in the early 1990s [15]. The basic concept and methods of this approach respites an idea that signifies the education which involves understanding and production of lexical phrases.…”

Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis

“…Therefore, many papers can be mentioned. Supervised methods have been widely exploited for this purpose, a classification algorithms with a wise feature extraction could achieve good results (Mohammad et al, 2013) (Hamdan et al, 2015a) (Hamdan et al,4 29).…”