Multilingual Sentiment Analysis for Web Text Based on Word to Word Translation

Fujihira, Keita; Horibe, Noriko

doi:10.1109/iiai-aai50415.2020.00025

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…It is vital to achieving this in trend and sentiment analysis for enormous data of social media, where massive amounts of instant messages are released every day, in order to fully utilise the overall processing power. The authors [18] proposed creating a multilingual sentiment analysis method that translates words for words using a sentiment dictionary in any language that the user speaks. Text morphological analysis, sentiment extraction from each word using a sentiment dictionary, and text sentiment extraction based on word emotion comprise.…”

Section: Santiment Analysis Challengesmentioning

confidence: 99%

A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media

Sarah Deen,

Mark Deen,

Danny

et al. 2024

IJMTST

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Sentiment analysis is discovering the current ideology opinion of a group of people and their thoughts. The Sentiment analysis based onthe natural reaction of people on social media platform to reflect their mantel status and state. The main poupose of sentiment analysis is to dealing with society's environment and its impact effects towards the media world and surrounding environment. However, this is the key task of understanding every part of the world. The evolution of feeling simulates the sentiment behaviours to shows different direction of reactions and feeling across time. It can help users obtain a more advanced and detailed understanding of the views and attitudes represented in the content provided by users. The development of social media platforms, such as journals, forums, blogs, micro-blogs, Twitter, and social networks, has fostered sentiment analysis. Competitive advantages for organizations are collecting corporate social media and implementing machine learning algorithms to get valuable insights. In this study, our tasks are to show Bag of Words (BoW) and Term-Frequency-Inverse-Document-Frequency (tf_idf) feature-based machine learning prediction models that can help with sentiment analysis and figure out what their customers need and want from company items. Market research is perhaps the most important field for sentiment analysis applications, aside from brand perception and customer opinion surveys and feedbacks. This study results analysis shows the crucial way of classifying social media tweets feedback into positive or negative categories via using the classifier as a baseline to demonstrate in what manner comments are important based on features for any business model and their result.

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Section: Santiment Analysis Challengesmentioning

confidence: 99%

A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media

Sarah Deen,

Mark Deen,

Danny

et al. 2024

IJMTST

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“…But, the outputs of backward LSTM are the reverse of the input sequence from L − 1 to L − m. Both the outputs of the forward and backward LSTM cell states are processed by the activation function of the reLU 𝛼 f . Equation (10) shows the threshold operations performance on the sample of input data. Here, the value which is less than zero is assumed as zero.…”

Section: Aspect Classification Using Relu-bilstmmentioning

confidence: 99%

“…The sentiment analysis has more impacts with respect to the modifications in trends, stock price, and product sales. 10 The human language is more complex and modified with respect to human expressions like thoughts, opinions, and perceptions which are explained in both written and oral language. Each and every language exist an individual set of rules which is related to words, terms, grammar, and syntax.…”

Section: Introductionmentioning

confidence: 99%

“…The web text contents can create the influence of real society, market research, and social analysis like politics. The sentiment analysis has more impacts with respect to the modifications in trends, stock price, and product sales 10 . The human language is more complex and modified with respect to human expressions like thoughts, opinions, and perceptions which are explained in both written and oral language.…”

Section: Introductionmentioning

confidence: 99%

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Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Kumar¹,

Sulochana

2022

Concurrency and Computation

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Aspect-based sentiment analysis has gained wide popularity due to its benefits of text extraction, classification, and ranking the overall sentiments of each feature extracted. However, the aspect-based feature extraction techniques often result in acquiring more number aspects that refer to the same feature which arises the need for aspect-based text classification. Since most of the existing techniques focus on monolingual aspect-based sentimental analysis, we planned to develop a multilingual aspect-based text classification for Indian languages. We perform the multilingual aspect-based text classification on different morphologically rich and complex languages such as Hindi, Tamil, Malayalam, Bengali, Urdu, Telugu, and Sinhalese. To achieve this objective, in this article we present an optimized rectified linear unit (reLU) layer-based bidirectional long short-term memory (reLU-BiLSTM) deep learning tool is developed. The parameters of the reLU-BiLSTM architecture are optimized using the local search-based five-element cycle optimization algorithm (LSFECO) optimization algorithm. Initially, the proposed model preprocesses the multilingual texts obtained from the reviews using different techniques such as tokenization, special character removal, text normalization and so forth. The discrete and categorical features from the different languages are initially extracted by applying the bidirectional encoder representations from transformers (BERT) model which processes the sentences in the text in a layer-by-layer manner. The context learning and word embeddings (aspects) present in the text are identified using different approaches such as word mover's distance, continuous Bag-of-Words (CBOW), and Cosine similarity. The LSFECO optimized reLU-BiLSTM architecture classifies the different aspects present in the embedding document to its corresponding classes (flowers, plants, animals, sports, politics, etc). The efficiency of the proposed methodology is evaluated using the text obtained from different text documents such as semantic relations from Wikipedia, Habeas Corpus (HC) Corpora, Sentiment Lexicons for 81 Languages, IIT Bombay English-Hindi Parallel Corpus, and Indic Languages Multilingual Parallel Corpus. When compared to conventional techniques, the proposed methodology outperforms them in terms of entropy, coverage, purity, processing time, accuracy, F1-score, recall, and precision.

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“…Improvements in statistical or neural machine translation systems eliminate the need to create separate monolingual Sentiment analysis models for separate languages [23][24][25][26][27][28][29][30][31][32]. For example, in [33], neural machine translation is used to convert the multilingual data set into English which is later classified as the English language model. If the source and target languages, computational expenses can be minimized by applying word-to-word translation [34].…”

Section: Introductionmentioning

confidence: 99%

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

2022

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We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.

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Multilingual Sentiment Analysis for Web Text Based on Word to Word Translation

Cited by 9 publications

References 9 publications

A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media

A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media

Local search five‐element cycle optimized reLU‐BiLSTM for multilingual aspect‐based text classification

Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning

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