Lexicon Based Document Level Sentiment Analysis on the Multilingual Dataset

Mathews, Deepa Mary; Abraham, Sajimon

doi:10.2139/ssrn.3349025

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Sentiment analysis techniques can be divided into three categories based on different levels: Document-level sentiment analysis aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment [11] . Mathews et al proposed a lexicon-based method to perform polarity calculation on the multilingual dataset which consists of a mix of reviews in English and Malayalam for sentiment [12] . The proposed methodology treats both types of lexicons differently and it gives more accurate results for sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

2020

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Governments and municipalities need to understand their citizens' psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents' real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The four parts interact with each other and provide users with a thorough human needs analysis based on their queries. We employed the proposed platform to investigate the reaction of people in New York State to the ongoing worldwide COVID-19 pandemic.

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Section: Related Workmentioning

confidence: 99%

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

2020

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“…This approach is crucial for accurately capturing sentiment in different linguistic contexts, as each language has unique semantic and syntactic structures that influence the way sentiment is expressed. The method is based on the principle that a one-size-fits-all approach is inadequate for multilingual sentiment analysis due to the large differences between languages [54].…”

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confidence: 99%

Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Sokolová,

Harahus,

Juhár

et al. 2024

Electronics

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The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

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