SentiView: A visual sentiment analysis framework

Abstract

In the past few years, micro-blogging platforms, such as twitter, are becoming most popular online social networks. Different opinions and news can be shared about various aspects and occasions using these micro-blogging platforms. Twitter is therefore considered as a rich source of data and it can be used for different text analysis and decision making tasks. The main focus of sentiment analysis is about text classification into positive/negative/neutral feelings based on the polarity of text. The opinions and thoughts on twitter feeds can be expressed in any language. Previous techniques have some limitations in the field of sentiment analysis such as low accuracy, sarcasm, and incorrect classification of tweets. The proposed research focuses on the existing difficulties and complications and presents a framework, for the sentiment detection of twitter feeds, which results in high accuracy and real time performance. There are various pre-processing steps that are applied on twitter feeds to refine them before feeding for sentiment classification. The pre-processing removes slangs and abbreviations with complete words. Three different classification techniques are then used; emoticon analysis, Bag of words and SentiWordNet. The experimental evaluation confirms that the proposed algorithm dynamically increases the precision, recall, f-measure and most importantly accuracy when compared with other similar techniques.

Automatic detection of satire in Twitter: A psycholinguistic-based approach

“…As mentioned above, the base resources used for this task are Hunspell, Twokenize, and NetLingo. These resources were selected because they have been widely and successfully used to pre-process tweets in other research works, such as [26], [27], [33], [34], [35], [36], [37].…”

Analysis of Various Sentiment Classification Techniques

“…Unwanted punctuation, new lines, ASCII code and stop word are removed and stemming is also performed as a part of preprocessing technique [6]. Stop word, stemming and tagging has been performed [7] [14]. Tokenization [9] divides given text into token.…”

“…GIS based approach [14] is proposed in which longitude and latitude is considered in classification process. The main aim of this visual classification is just to provide view for easy understanding.…”

“…Emotions of tweets generate best performance so it is used to improve accuracy in first emotion classifier. First emotion classifier is performed if generated result is neutral then Bag of words model is performed and if it still generates neutral result then SentiWordNet classifier is performed [14].…”

Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies

“…Uni-gram TF-IDF and Bi-gram TF were used, as feature extractors in the experiment, out of which uni-gram provides better performance. Furthermore, Khan et al [15] classified tweets into positive, negative and neutral classes using various approaches such as emot-icons, bag of words and SentiWordNet.…”