Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models

• URL: http://arxiv.org/abs/2409.12840v1
• Date: Thu, 19 Sep 2024 15:31:12 GMT
• Title: Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models
• Authors: Muhammad Raees, Samina Fazilat,
• Abstract summary: This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral.
• Score: 1.342834401139078
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Sentiment analysis possesses the potential of diverse applicability on digital platforms. Sentiment analysis extracts the polarity to understand the intensity and subjectivity in the text. This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. The categorization identifies the informative words inside a text and specifies the quantitative ranking of the polarity of words. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral. Twitter sentiment dataset containing 1.6 million unprocessed tweets is used with lexicon-based methods like Text Blob and Vader Sentiment to introduce the neutrality measure on text. The analysis of lexicons shows how the word count and the intensity classify the text. A comparative analysis of machine learning models, Naiive Bayes, Support Vector Machines, Multinomial Logistic Regression, Random Forest, and Extreme Gradient (XG) Boost performed across multiple performance metrics. The best estimations are achieved through Random Forest with an accuracy score of 81%. Additionally, sentiment analysis is applied for a personality judgment case against a Twitter profile based on online activity.

Related papers

• A Comparison of Lexicon-Based and ML-Based Sentiment Analysis: Are There Outlier Words? [14.816706893177997]
  In this paper we compute sentiment for more than 150,000 English language texts drawn from 4 domains. We model differences in sentiment scores between approaches for documents in each domain using a regression. Our findings are that the importance of a word depends on the domain and there are no standout lexical entries which systematically cause differences in sentiment scores.
  arXiv  Detail & Related papers  (2023-11-10T18:21:50Z)
• Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis [6.596002578395151]
  ChatGPT is a new product of OpenAI and has emerged as the most popular AI product. This study explores the use of ChatGPT as a tool for data labeling for different sentiment analysis tasks.
  arXiv  Detail & Related papers  (2023-06-18T12:20:42Z)
• A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing [1.0499611180329804]
  This study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text. The proposed method examined all the unique features of the related expressions within a text to resolve the contextual usage of the sentence.
  arXiv  Detail & Related papers  (2023-02-05T03:58:45Z)
• Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis [64.70116276295609]
  SentiWSP is a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-18T12:25:29Z)
• Textual Entailment Recognition with Semantic Features from Empirical Text Representation [60.31047947815282]
  A text entails a hypothesis if and only if the true value of the hypothesis follows the text. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair.
  arXiv  Detail & Related papers  (2022-10-18T10:03:51Z)
• Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets [0.10499611180329801]
  The paper proposes a novel algorithm that graphically models social media text using multi-layered networks (MLNs) in order to better encode relationships across independent sets of tweets. State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features. Results show that not only does the MLTA predict from a larger set of possible emotions, delivering a more accurate sentiment compared to the standard positive, negative or neutral, it also allows for accurate group-level predictions of Twitter data.
  arXiv  Detail & Related papers  (2022-07-02T20:26:55Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles [62.997667081978825]
  This paper presents a novel method for vector representation of text meaning based on information theory. We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus. We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
  arXiv  Detail & Related papers  (2021-04-26T20:37:13Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text [68.8204255655161]
  This work introduces a new method to consider subjectivity and general context dependency in text analysis. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
  arXiv  Detail & Related papers  (2020-05-12T21:26:04Z)
• Quality of Word Embeddings on Sentiment Analysis Tasks [0.0]
  We compare performance of a dozen of pretrained word embedding models on lyrics sentiment analysis and movie review polarity tasks. According to our results, Twitter Tweets is the best on lyrics sentiment analysis, whereas Google News and Common Crawl are the top performers on movie polarity analysis.
  arXiv  Detail & Related papers  (2020-03-06T15:03:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.