AWESSOME: An unsupervised sentiment intensity scoring framework using neural word embeddings

Amal Htait, Leif Azzopardi

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

Sentiment analysis (SA) is the key element for a variety of opinion and attitude mining tasks. While various unsupervised SA tools already exist, a central problem is that they are lexicon-based where the lexicons used are limited, leading to a vocabulary mismatch. In this paper, we present an unsupervised word embedding-based sentiment scoring framework for sentiment intensity scoring (SIS). The framework generalizes and combines past works so that pre-existing lexicons (e.g. VADER, LabMT) and word embeddings (e.g. BERT, RoBERTa) can be used to address this problem, with no require training, and while providing fine grained SIS of words and phrases. The framework is scalable and extensible, so that custom lexicons or word embeddings can be used to core methods, and to even create new corpus specific lexicons without the need for extensive supervised learning and retraining. The Python 3 toolkit is open source, freely available from GitHub (https://github.com/cumulative-revelations/awessome ) and can be directly installed via pip install awessome.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Proceedings
EditorsDjoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani
Place of PublicationCham, Switzerland
PublisherSpringer
Chapter56
Pages509-513
Number of pages5
Volume12657
ISBN (Electronic)9783030722401
ISBN (Print)9783030722395
DOIs
Publication statusPublished - 28 Mar 2021
EventEuropean Conference on Information Retrieval 2021 - Online, Lucca, Italy
Duration: 28 Mar 20211 Apr 2021
Conference number: 43
https://www.ecir2021.eu/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Information Retrieval 2021
Abbreviated titleECIR 2021
Country/TerritoryItaly
CityLucca
Period28/03/211/04/21
Internet address

Keywords

  • sentiment intensity
  • pre-trained language model
  • lexicon
  • BERT
  • VADER

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