A novel Tag Score (T_S) model with improved K-means for clustering tweets

Poomagal, S.; Malar, B.; Hassan, Jawad; Kishor, R

doi:10.1007/s12046-020-01359-5

Cited by 3 publications

(3 citation statements)

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“…Reference [36] proposed a Tag Score model with improved K-mean algorithm for tweets clustering into positive, negative, or neutral. They grouped semantically similar features from BOW into tags (addressed dimensionality reduction issue), scores of sentiment words were modified and, then, centroids of clusters were chosen based on the sentiment scores.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It would prepare the real-time twitter corpus for further analysis and helps in the reduction of feature space too by the removal of unnecessary elements from the tweet. Several early works highlights the significance of data pre-processing before clustering [13], [16], [22], [26], [36].…”

Section: B Data Pre-processing (Tweet Normalization)mentioning

confidence: 99%

“…In contrast to supervised machine models, K-mean has the ability to classify data points without having firstly trained on the labelled dataset. Clustering is an unsupervised approach used to find sub-groups (clusters) within a dataset based on the underlying patterns [9], [16], [36]. Objects in the same cluster are more similar to each other than the other cluster.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

Gupta¹,

Joshi²

2021

IJCDS

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Section: Literature Reviewmentioning

confidence: 99%

Section: B Data Pre-processing (Tweet Normalization)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

Gupta¹,

Joshi²

2021

IJCDS

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Sentiment Thesaurus, Synset and Word2Vec Based Improvement in Bigram Model for Classifying Product Reviews

et al. 2022

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Classifying product reviews is one of the tasks in Natural Language Processing by which the sentiment of the reviewer towards a product can be identified. This identification is useful for the growth of the business by increasing the number of satisfied customers through product quality improvement. Bigram models are more popular in performing this classification since it considers the occurrence of two words consecutively in the reviews. In the existing works on bigram models, semantically similar words to the words present in bigrams are not considered. As the reviewers use different words with the same meaning to express their feeling, we proposed improved bigram models in which semantically similar words to the words in bigrams are also used for classifying the reviews. In the proposed models, sentiment polarity thesaurus is constructed by including sentiment words and their synonyms. The combinations of constructed thesaurus, Synset and Word2Vec are used for extracting synonyms for the words in the reviews. Performance of the proposed models is compared with the traditional bigram model and state-of-the-art methods. It is observed from the results that our models are able to achieve better performance than traditional model and recent methods.

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