Acquiring Sentiment from Twitter using Supervised Learning and Lexicon-based Techniques

A Supervised Method for Constructing Sentiment Lexicon in Persian Language

Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...

Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA

Incremental Learning on Sentiment Analysis Using Weakly Supervised Learning Techniques

Due to the advanced technologies of Web 2.0, people are participating in and exchanging opinions through social media sites such as Web forums and Weblogs etc., Classification and Analysis of such opinions and sentiment information is potentially important for both service and product providers, users because this analysis is used for making valuable decisions. Sentiment is expressed differentl...

Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis

With the booming of microblogs on the Web, people have begun to express their opinions on a wide variety of topics on Twitter and other similar services. Sentiment analysis on entities (e.g., products, organizations, people, etc.) in tweets (posts on Twitter) thus becomes a rapid and effective way of gauging public opinion for business marketing or social studies. However, Twitter's unique char...

Sentiment Analysis on Twitter through Topic-Based Lexicon Expansion

Supervised learning approaches are domain-dependent and it is costly to obtain labeled training data from different domains. Lexiconbased approaches enjoy stable performance across domains, but often cannot capture domain-dependent features. It is also hard for lexiconbased classifiers to identify the polarities of abbreviations and misspellings, which are common in short informal social text b...