Umigon: Sentiment Analysis for Tweets Based on Lexicons and Heuristics

  title={Umigon: Sentiment Analysis for Tweets Based on Lexicons and Heuristics},
  author={Clement Levallois},
Umigon is developed since December 2012 as a web application providing a service of sentiment detection in tweets. It has been designed to be fast and scalable. Umigon also provides indications for additional semantic features present in the tweets, such as time indications or markers of subjectivity. Umigon is in continuous development, it can be tried freely at Its code is open sourced at: 

Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons

To complement traditional sentiment dictionaries, this work presents a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter, and shows that complementary lexicons increase the performance of three state-of-the-art sentiment systems.

Sentiment Resources: Lexicons and Datasets

This chapter explores the philosophy, execution and utility of popular sentiment lexicons and datasets, and provides a detailed description of existing sentiment and emotion lexicons, and the trends underlying research in lexicon generation.

A Practical Guide to Sentiment Analysis

The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

A novel system for lexicon expansion that automatically extracts the more relevant and up to date terms on several different domains and then assesses their sentiment through Twitter is proposed, providing evidence that the Lexicon expansion system can extract and determined the sentiment of terms for domain and time specific corpora in a fully automatic form.

An evaluation of machine translation for multilingual sentence-level sentiment analysis

Evaluating existing efforts proposed to do language specific sentiment analysis for English suggests that simply translating the input text on a specific language to English and then using one of the existing English methods can be better than the existing language specific efforts evaluated.

Building a Semi-Supervised Dataset to Train Journalistic Relevance Detection Models

  • N. GuimarãesÁ. Figueira
  • Computer Science
    2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
  • 2017
This work proposes an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets), based on the predictability of the content in Twitter accounts, and builds relevance detection models, combining text, entities, and sentiment features.

Extending a Fuzzy Polarity Propagation Method for Multi-Domain Sentiment Analysis with Word Embedding and POS Tagging

An existing approach based on the propagation of fuzzy polarities over a semantic graph capturing background linguistic knowledge is extended to learn concept polarities with respect to various domains and their uncertainty from labeled datasets.

Opinion Classification in Conversational Content Using N-grams

The paper introduces the problem of opinion classification related to conversational content. It describes briefly various approaches known in this field. The focus is on a novelty method which has

An evaluation of sentiment analysis for mobile devices

This paper compares the performance of 14 sentence-level sentiment analysis methods in the mobile environment and unveils methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory.

A survey of multimodal sentiment analysis



Twitter Sentiment Analysis: The Good the Bad and the OMG!

This paper evaluates the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging, and uses existing hashtags in the Twitter data for building training data.

Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.

SemEval-2013 Task 2: Sentiment Analysis in Twitter

Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask.

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of

Twitter Sentiment Analysis: The Good the Bad and the OMG! Proceedings of ICWSM

  • Twitter Sentiment Analysis: The Good the Bad and the OMG! Proceedings of ICWSM
  • 2011