Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

Gupta, Itisha; Joshi, Nisheeth

doi:10.12785/ijcds/100150

Cited by 8 publications

(6 citation statements)

References 22 publications

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“… ML-based methods [30], [31], [19]. In this case, it trains a model using labeled data, where the labeled data contains text and the corresponding sentiment (positive, negative, neutral) and then the model is used to predict the sentiment of un-seen text.…”

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

“…The SA algorithms are used to classify the polarity of a text as positive, negative, or neutral, based on the sentiment expressed in the text. Among their various contributions to SA, the specialized literature mentions:  Text classification [38], [31], [22]: SA algorithms are often used to classify text into different sentiment categories, such as positive, negative, or neutral. This is typically done using supervised learning algorithms, like Naive Bayes [2], [39], Support Vector Machine (SVM) [40], [41], Logistic Regression [41], [42] or DL algorithms like BERT, LSTM and CNN.…”

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

“… Opinion mining [31], [33], [22]: SA algorithms can also be employed for extracting and understanding opinions and sentiments from text. This is typically done using NLP techniques, such as sentiment lexicons, sentiment ontologies, and sentiment-bearing terms.…”

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

“…Positive and negative sentiments have both been subjected to text clustering by [17] in order to identify the main concerns that individuals have with regards to AI Ethical challenges. Other researchers [31] employ text clustering in a novel approach to extract agrarians' recommendations to boosting crop yields by informing farmers via SA on the most recent agricultural inputs. Overall, the specific algorithm used in SA depends on the problem, the resources available and the desired accuracy.…”

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

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Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

Pavaloaia

2024

IJACSA

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Based on the high dynamic of Sentiment Analysis (SA) topic among the latest publication landscape, the current review attempts to fill a research gap. Consequently, the paper elaborates on the most recent body of literature to extract and analyze the papers that elaborate on the clustering algorithms applied on social media datasets for performing SA. The current rapid review attempts to answer the research questions by analyzing a pool of 46 articles published in between Dec 2020 -Dec 2023. The manuscripts were thoroughly selected from Scopus (Sco) and WebOf-Science (WoS) databases and, after filtering the initial pool of 164 articles, the final results (46) were extracted and read in full.

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Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

Section: A the Most Employed Clustering Algorithms Their Benefits And...mentioning

confidence: 99%

See 2 more Smart Citations

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

Pavaloaia

2024

IJACSA

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“…The main goal of sentiment analysis is classifying polarity of opinions [7] [8] [9]. State-of-the-art systems for sentiment analysis deal mainly with three levels of annotation granularity towards the input: document, sentence, aspect, or phrase (word) [10].…”

Section: Introductionmentioning

confidence: 99%

AT-ODTSA: a Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis

Sahmoud¹,

Abudalfa²,

Elmasry³

2022

IJCDS

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In the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets. Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model.

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Multi-class E-mail Classification with a Semi-Supervised Approach Based on Automatic Feature Selection and Information Retrieval

Fernández

Errecalde

2022

Communications in Computer and Information Science

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Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

Cited by 8 publications

References 22 publications

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

AT-ODTSA: a Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis

Multi-class E-mail Classification with a Semi-Supervised Approach Based on Automatic Feature Selection and Information Retrieval

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