Sentiment analysis on tweets for social events

Abstract

Design of complex artifacts, systems, processes, and services requires the cooperation of multidisciplinary design teams. The 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2013) provides a forum for researchers and practitioners involved in different but related domains to confront research results and discuss key problems. The scope of CSCWD 2013 includes the research and development fields of collaboration technologies and applications to the design of processes, products, systems, and services in industries and societies. Such technologies include collaboration methods, mechanisms, protocols, software tools, platforms, and services that support collaboration and coordination among people, software and hardware systems. Related fields of research include collaborative workflows, collaborative virtual environments, collaborative workspaces, collaborative wireless sensor networks, interoperability, ontology and semantics, security and privacy, as well as social aspects and human factors related to collaboration.

Analysis of Various Sentiment Classification Techniques

“…Unigram feature extraction technique has been used to extract feature and feature vector list is produced [11]. Opinion words are extracted using Wilson lexicon list [12]. Unigram, Bigram, Unigram with bigram and Unigram with Pos tagging technique are used as part of feature selection to extract features and emotions are taken as noisy label to improve the accuracy level [13].…”

“…Semantic orientation is determined by assigning +1 to positive word,+0.5 to weak positive word, -1 to negative word, -0.5 to weak negative word and 0 to neutral word as semantic orientation score. Sentence Sentiment Scoring Function (SSSF) calculates the score of orientation of sentiment for each entity e i in s. Entity Sentiment Aggregation Function (ESAF) calculates the total sentiment scores for an entity e i [12]. Emotion classifier, Bag of word classifier and SentiWordNet classifier have been proposed.…”

Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis

“…They have used Wilson opinion lexicon list for semantic orientation. It is used to predict prior public opinion [8]. The aim of paper is to find best effective features which provide better result and also provide better feature selection method.…”

Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies

“…They used 3 Million tweets and constructed a linear regression model. Similarly, Zhou et al [8] developed a Tweet Sentiment Analysis Model (TSAM) which was able to successfully determine the societal interest as well as general peoples' opinions with respect to a social event (Australian federal elections). Sriram et al [9] classified the tweets using a small set of domain-specific features extracted from the authors profile along with the text.…”