Artificial Intelligence Techniques in Text and Sentiment Analysis

Rao, Patruni Muralidhara; Angadi, Anupama; Gorripati, Satya Keerthi; Saraswathi, P. Vidhya

doi:10.4018/978-1-6684-6242-3.ch009

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Currently, mathematical models can be taught to recognise fake news. Sentiment analysis works on a similar principle (Patruni, Angadi, Gorripati, & Saraswathi, 2022), thanks to which, for example, it is possible to determine the tone -positive, negative, or neutral-of reviews or readers' opinions about publications in the media.…”

Section: Issn: 2659-9538mentioning

confidence: 99%

Metodología en la era de los algoritmos

2023

C&M

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A pesar del dinámico desarrollo de los llamados nuevos medios -o, como diría Paul Levinson: nuevos nuevos medios-, la metodología sigue siendo esencial. La comunicación actual no es tan aparente como cuando los fundadores de la ciencia de los medios crearon sus modelos de comunicación. El inicio de ese proceso fue el aumento de popularidad de la tendencia web 2.0, que supone, por ejemplo, sustituir el consumo pasivo de mensajes mediáticos elaborados por periodistas profesionales por la participación activa de los receptores en la creación de contenidos. La omnipresente convergencia también nos aleja de la obviedad de los llamados medios heredados. Con los métodos tradicionales de investigación de medios, ¿podemos analizar un periódico que publica su serie de podcasts, difunde en directo la cobertura de acontecimientos relevantes y de actualidad en las redes sociales y tiene su propio estudio de televisión? Estas preguntas son más importantes ahora que los "nuevos medios" ya no son "nuevos", y los estudiantes de periodismo, medios y comunicación social nacieron cuando Wikipedia ya era famosa, Facebook funcionaba y consolidaba su poder, y YouTube apenas preparaba su lanzamiento.

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Section: Issn: 2659-9538mentioning

confidence: 99%

Metodología en la era de los algoritmos

2023

C&M

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“…Companies can use sentiment analysis to discover patterns in customer preferences and recommendations for new products and services based on that data. Natural language processing (NLP) and artificial intelligence (AI) applications are also able to quickly gather website reviews to gain insight into how consumers view a particular product or services [3]. This can help companies understand their customers better and inform strategic decisions in terms of product offerings and pricing.…”

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Implicit aspect based sentiment analysis for restaurant review using LDA topic modeling and ensemble approach

2023

IJATEE

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The internet is a global web that links millions of people, and therefore, it has become the hub of modern-day social life [1]. It is widely appreciated for providing an open platform where individuals can connect and express their opinions on anything and everything in their environment. However, it can be difficult for an individual to trawl through each online resource. Therefore, approaches for evaluating this collection of documents are essential. *Author for correspondenceAs a result, techniques like feature (aspect) extraction and sentiment analysis play a crucial role in leading individuals, businesses, and organizations in the right direction. This work seeks to provide insight into features concerning the desired domain.Sentiment analysis is the act of examining whether a piece of writing (e.g. tweets or reviews about books, movies, restaurants etc.) is negative, positive, or neutral in its tone. It's also known as sentiment mining and involves analyzing opinions, assessments, feelings or attitudes about topics, people or organizations [2]. For example: "I love the garlic noodles at your restaurant" could be classified as a

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“…The lexicon-related method can be classified into 2 methods they are dictionary and corpus-based. In a dictionary-related method, the sentiment classification can be done through a dictionary of terms identified in SentiWordNet and WordNet [5]. Conversely, the corpus-related analysis methods rely on the statistical analysis of contents by using methods linked to the Conditional Random Field (CRF), k-nearest neighbours, and Hidden Markov Models (HMM).…”

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Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Al-Fatlawy

2023

Journal of Smart Internet of Things

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Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.

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Artificial Intelligence Techniques in Text and Sentiment Analysis

Cited by 3 publications

References 25 publications

Metodología en la era de los algoritmos

Metodología en la era de los algoritmos

Implicit aspect based sentiment analysis for restaurant review using LDA topic modeling and ensemble approach

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

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