Lexicon Generation Using Genetic Algorithm for Aspect-Based Sentiment Analysis

Mowlaei, Mohammad Erfan; Abadeh, Mohammad Saniee; Keshavarz, Hamidreza

doi:10.1109/ines.2018.8523902

Cited by 1 publication

(1 citation statement)

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“…They built a domain-specific lexicon by introducing a bigram algorithm with a proposed strategy for developing some new corpora. Mowlaei et al [22] developed a Lexicon using a genetic algorithm and this fits aspectlevel problems. They have used two Lexicons, the first one is the intensity Lexicon (NRC Hashtag, AFINN, and Sentiment 140) and another one is the polarity Lexicon (Liu opinion Lexicon).…”

Section: Introductionmentioning

confidence: 99%

Lexicon-based sentiment analysis for Kannada-English code-switch text

Chundi

Hulipalled²,

Simha³

2023

IJ-AI

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Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.

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Section: Introductionmentioning

confidence: 99%