A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

Alexandridis, Georgios; Varlamis, Iraklis; Korovesis, Konstantinos; Tsantilas, Panagiotis

doi:10.3390/info12080331

Cited by 29 publications

(21 citation statements)

References 48 publications

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“…They used several feature extractors, including the GloVe model, to efficiently detect and capture relevant data from given tweets. Alexandridis et al [27] used various language models to represent social media texts and Greek language text classifiers, using word embedding implemented by the GloVe model, to detect the polarity of opinions expressed on social media. The GloVe model has also been used in sentiment analysis models, often associated with a recurrent neural network module like long sort-term memory (LSTM) or GRU [6], [28], [29].…”

Section: Glovementioning

confidence: 99%

Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

Chiny

Chihab

Lahcen

et al. 2023

IJ-AI

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<span lang="EN-US">Word embedding has become the most popular method of lexical description in a given context in the natural language processing domain, especially through the word to vector (Word2Vec) and global vectors (GloVe) implementations. Since GloVe is a pre-trained model that provides access to word mapping vectors on many dimensionalities, a large number of applications rely on its prowess, especially in the field of sentiment analysis. However, in the literature, we found that in many cases, GloVe is implemented with arbitrary dimensionalities (often 300d) regardless of the length of the text to be analyzed. In this work, we conducted a study that identifies the effect of the dimensionality of word embedding mapping vectors on short and long texts in a sentiment analysis context. The results suggest that as the dimensionality of the vectors increases, the performance metrics of the model also increase for long texts. In contrast, for short texts, we recorded a threshold at which dimensionality does not matter.</span>

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Section: Glovementioning

confidence: 99%

Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

Chiny

Chihab

Lahcen

et al. 2023

IJ-AI

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“…Alexandridis [14] mentioned that social media is currently also the largest storehouse of public opinion, not least in the government. The main cause is the fact of public expression in responding to government issues in the social media dimension.…”

Section: Social Mediamentioning

confidence: 99%

Public Engagement in Social Media Opinion: PPKM Policy Level 3 Christmas and New Year 2022

Sukarno¹,

Nur²

2022

Advances in Social Science, Education and Humanities Research

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This research aims to find out public opinion on the cancellation of PPKM Level 3 policy at Christmas and New Year (Nataru) in the social media dimension. The research method used is qualitative by using a case study approach to PPKM level 3 policy. Primary data sources are social media data and secondary data that includes articles and news of the mass media. Data analysis using Nvivo 12plus software presented in the form of crosstab analysis, User account activity, user response folder, and word cloud analysis. The results of this study showed that public opinion in the cancellation of PPKM level 3 majority agreed with segmentation believing in government policies. Although there are some opinions that reject because it is considered that there is no consistency of the government in describing the decision. Some other opinions such as neutral, unclear (have no relevance to the topic of discussion) and opinions supported and reject. The analysis of user account activity showed that the highest intensity was in the @hilmi28 account with a percentage of 15.63% more discussing the cancellation of PPKM Level 3. Furthermore, based on the results of the analysis of user response folders that show many users or users involved in the discussion, the highest distribution is in Indonesia and several of such as America. North, Africa, Australia, and Europe. Result from Word cloud analysis, in the topic of PPKM level 3 words policy that often appears, namely PPKM, Nataru, government, level, and holidays as a word material that is used as public discussion material.

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“…This model combines traditional deep learning techniques and introduces an attention mechanism and a knowledge management system, which can further improve the accuracy of classification. Alexandridis et al [21] tested the performance of various text classifiers such as feed-forward neural networks in Greek sentiment analysis, and the experimental results were clearly displayed.…”

Section: Research On Sentiment Analysis Based On Deep Learningmentioning

confidence: 99%

Weibo Text Sentiment Analysis Based on BERT and Deep Learning

et al. 2021

Applied Sciences

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With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

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A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

Cited by 29 publications

References 48 publications

Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

Public Engagement in Social Media Opinion: PPKM Policy Level 3 Christmas and New Year 2022

Weibo Text Sentiment Analysis Based on BERT and Deep Learning

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