3D Visualization of Sentiment Measures and Sentiment Classification using Combined Classifier for Customer Product Reviews

Urologin, Siddhaling; Thomas, Sunil

doi:10.14569/ijacsa.2018.090508

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Facebook API is incorporated to classify sentiments written in the German language. Valence aware dictionary for sentiment reasoning (VADER) is presented in [25] for sentiment classification. A total of 12,500 user reviews are gathered from amazon to construct a database.…”

Section: B Sentiment Analysis Using Lexicon-based Approachmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist health-care professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learningbased approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.

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Section: B Sentiment Analysis Using Lexicon-based Approachmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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“…Classical machine learning opens up the way to learn hidden patterns in data through several mathematical models and overcome the drawbacks of lexicon based approaches in handling words with implicit emotion expressions. Most studies in this approach of textual emotion detection are designed as supervised multi-class tasks and some as multi-label/target tasks [7], with learning models like Support Vector Machine (SVM) [34], Naïve Bayes [35], multi-layer perceptron [36], logistic regression [37,38] etc. Features used across such approaches can be broadly categorized as Linguistic features [34,39], Symbol level features [32], and Affective features [32,40].…”

Section: Machine Learning Based Approachesmentioning

confidence: 99%

Readers’ affect: predicting and understanding readers’ emotions with deep learning

et al. 2022

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Emotions are highly useful to model human behavior being at the core of what makes us human. Today, people abundantly express and share emotions through social media. Technological advancements in such platforms enable sharing opinions or expressing any specific emotions towards what others have shared, mainly in the form of textual data. This entails an interesting arena for analysis; as to whether there is a disconnect between the writer’s intended emotion and the reader’s perception of textual content. In this paper, we present experiments for Readers’ Emotion Detection through multi-target regression settings by exploring a Bi-LSTM-based Attention model, where our major intention is to analyze the interpretability and effectiveness of the deep learning model for the task. To conduct experiments, we procure two extensive datasets REN-10k and RENh-4k, apart from using a popular benchmark dataset from SemEval-2007. We perform a two-phase experimental evaluation, first being various coarse-grained and fine-grained evaluations of our model performance in comparison with several baselines belonging to different categories of emotion detection, viz., deep learning, lexicon based, and classical machine learning. Secondly, we evaluate model behavior towards readers’ emotion detection assessing attention maps generated by the model through devising a novel set of qualitative and quantitative metrics. The first phase of experiments shows that our Bi-LSTM + Attention model significantly outperforms all baselines. The second analysis reveals that emotions may be correlated to specific words as well as named entities.

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3D Visualization of Sentiment Measures and Sentiment Classification using Combined Classifier for Customer Product Reviews

Cited by 2 publications

References 24 publications

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Readers’ affect: predicting and understanding readers’ emotions with deep learning

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