A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

: Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral

• Publication year

  2023

• Venue

  Brazilian Archives of Biology and Technology

• Publication date

  2023-07-17

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1590/1678-4324-2023220654
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Sentiment Analysis with Machine Learning Methods on Social Media

  2020cited by this paper
• Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

  2019cited by this paper
• Survey on Sentiment Analysis using Twitter Dataset

  2018cited by this paper
• Sentiment analysis: a review and comparative analysis over social media

  2018cited by this paper
• A survey of sentiment analysis techniques

  2017influential reference
• A survey on classification techniques for opinion mining and sentiment analysis

  2017cited by this paper
• Document Level Sentiment Analysis from News Articles

  2017cited by this paper
• Sentiment analysis of product reviews: A review

  2017cited by this paper
• Application of machine learning techniques to sentiment analysis

  2016cited by this paper
• Social‐media‐based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security

  2016cited by this paper
• Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts

  2016cited by this paper
• Public-Opinion Sentiment Analysis for Large Hydro Projects

  2016cited by this paper
• Sentiment Analysis of Twitter Data : A Survey of Techniques

  2016cited by this paper
• A forecast model for stock trading using support vector machine

  2016cited by this paper
• Prominent feature extraction for review analysis: an empirical study

  2016cited by this paper
• Predicting wins and spread in the Premier League using a sentiment analysis of twitter

  2016cited by this paper
• Aspect extraction in sentiment analysis: comparative analysis and survey

  2016cited by this paper
• A sentiment analysis of U.S. local government tweets: The connection between tone and citizen involvement

  2015cited by this paper
• Expert Recommendations Based on Opinion Mining of User‐Generated Product Reviews

  2015cited by this paper
• A novel deterministic approach for aspect-based opinion mining in tourism products reviews

  2014cited by this paper
• A user opinion and metadata mining scheme for predicting box office performance of movies in the social network environment

  2013cited by this paper
• A multi-stage method for content classification and opinion mining on weblog comments

  2013cited by this paper
• Sentiment Classification of Drug Reviews Using a Rule-Based Linguistic Approach

  2012influential reference
• A two-stage framework for cross-domain sentiment classification

  2011cited by this paper
• Using text mining and sentiment analysis for online forums hotspot detection and forecast

  2010cited by this paper
• Aspect-level news browsing: understanding news events from multiple viewpoints

  2010cited by this paper
• Cross-domain sentiment classification using a two-stage method

  2009cited by this paper
• NewsCube: delivering multiple aspects of news to mitigate media bias

  2009cited by this paper
• A holistic lexicon-based approach to opinion mining

  2008influential reference
• More than Words: Quantifying Language to Measure Firms' Fundamentals

  2008cited by this paper
• Giving Content to Investor Sentiment: The Role of Media in the Stock Market

  2005cited by this paper
• Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis

  2005cited by this paper

• No citing papers are available for this paper.