An efficient sentimental analysis using hybrid deep learning and optimization technique for Twitter using parts of speech (POS) tagging

Divyapushpalakshmi, M.; Ramalakshmi, R.

doi:10.1007/s10772-021-09801-7

Cited by 21 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In keeping with an NLP model, the tool processes a given corpus word by word and sentence by sentence, retrieving an annotated corpus and showing variables such as lemmas, the roots of words as they are recorded in a dictionary; POS tags, taggings that label words by their part of the speech; and features, the temporal forms of nouns and verbs. As other studies have proven before, these methods are useful for a better understanding of sentiment and mood on social media 26 , 27 .…”

Section: Methodsmentioning

confidence: 84%

A text mining analysis of human flourishing on Twitter

Loureda

Hernández-Baqueiro²,

Tamés-Muñoz³

2023

Sci Rep

View full text Add to dashboard Cite

The power of social media in spreading the idea of wellbeing has already been addressed by several psychologists and scholars through the analysis of the vocabulary; however, the use of the human flourishing (HF) concept in such platforms has not yet been analyzed. This study addresses such a topic by analyzing more than 600 thousand Twitter messages posted by a community of users who associate themselves with HF and comparing them to more than 400 thousand messages in other Twitter lists. The study aims to identify the HF users’ interests, the richness in their vocabulary, the feelings and emotions that they share, and the grammar used in their constructions. Such an analysis was conducted through text mining computational methods, including sentiment analysis, natural language processing (NLP), and topic modeling. The results revealed that although HF users employ average vocabulary diversity, they share more positive emotions, and a greater variety of emojis. They also tended to discuss different topics, from more spiritual and health-related subjects to more practical matters related to work and success. Finally, they generally wrote from an empathetic state of mind, caring about people’s day-to-day feelings and about the world.

show abstract

Section: Methodsmentioning

confidence: 84%

A text mining analysis of human flourishing on Twitter

Loureda

Hernández-Baqueiro²,

Tamés-Muñoz³

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…e comment data with different sentiment polarities were filtered by a sentiment analysis model, and the experimental results showed that text data with negative sentiment were more in the online rumor detection task compared with text data with the positive and neutral sentiment. By proposing a new sentence vector representation instead of the word vector feature representation, multiple sets of comparison experiments show that the newly proposed method can improve the performance of the model to a certain extent [18].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers.

show abstract

“…According to [19,25], the rules generated mostly depend on linguistic features of the language, such as lexical, morphological, and syntactical information. Linguistic experts may construct these rules or use machine learning on an annotated corpus [10,11]. The first way of getting rules is tedious, prone to error, and time-consuming.…”

Section: Rule Basedmentioning

confidence: 99%

“…So, Part of Speech (POS) Tagging is a notable NLP topic that aims in assigning each word of a text the proper syntactic tag in its context of appearance [4][5][6][7][8]. Part-of-speech (POS) tagging, also called grammatical tagging, is the automatic assignment of part-of-speech tags to words in a sentence [9][10][11]. A POS is a grammatical classification that commonly includes verbs, adjectives, adverbs, nouns, etc.…”

mentioning

confidence: 99%

Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

View full text Add to dashboard Cite

Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

show abstract

An efficient sentimental analysis using hybrid deep learning and optimization technique for Twitter using parts of speech (POS) tagging

Cited by 21 publications

References 25 publications

A text mining analysis of human flourishing on Twitter

A text mining analysis of human flourishing on Twitter

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

Part of speech tagging: a systematic review of deep learning and machine learning approaches

Contact Info

Product

Resources

About