Ensemble Approach for Twitter Sentiment Analysis

Tiwari, Dimple; Singh, Nanhay

doi:10.5815/ijitcs.2019.08.03

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…The work of Tiwari and Nanhay et al [16] introduces the Twitter sentiment analysis ensemble process. Two ensemble methods, AdaBoost and Extra Tree, are used to increase the accuracy of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

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The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/ learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student's fraternity as it is during this COVID-19 pandemic period.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sirajudeen

Duraisamy

Haleema

et al. 2021

Intelligent Computing and Innovation on Data Science

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show abstract

“… Porter-Stemmer: is a procedure of separating common inflexional and morphological endings from the words. It extracts the similar or stemmed words that are semantically similar and then replaces them with single root words [ 50 ]. …”

Section: Proposed Methodologymentioning

confidence: 99%

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

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Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks.

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“…Extra Tree is an ensemble method that works well for sentiment analysis outperforming basic algorithms such as KNN, Naive Bayes, and Decision Tree [18]. Extra Trees are also capable of working on unbalanced data [19]. As with the Random Forest method, Extra Trees is an ensemble form of the basic Decision Tree algorithm.…”

Section: ) Extra Treementioning

confidence: 99%

The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

Khomsah¹,

Ramadhani

Wijaya³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators.

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Ensemble Approach for Twitter Sentiment Analysis

Cited by 12 publications

References 22 publications

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

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