SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity
            Detection Using Rule Based Feature Selection

S, Angel Deborah; Rajalakshmi, S.; Rajendram, S Milton; Mirnalinee, T. T.

doi:10.18653/v1/s18-1048

Cited by 4 publications

(1 citation statement)

References 9 publications

(7 reference statements)

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“…They also extended the training set by using an external dataset provided by the competition to learn a better representation of emojis and #hashtags. The other studies also applied DL networks, such as MLP [68], LSTM [60].…”

Section: Literature Reviewmentioning

confidence: 99%

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

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Sidorov³

et al. 2023

IEEE Access

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Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users' emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).

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Section: Literature Reviewmentioning

confidence: 99%

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Ameer

Bölücü

Sidorov³

et al. 2023

IEEE Access

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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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Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system

Rajalakshmi

Milton

Mirnalinee

2023

AIC

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Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users’ individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users’ satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user’s interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user’s preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms.

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SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection

Cited by 4 publications

References 9 publications

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

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

Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system

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