SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets
            Using MultiLayer Perceptron

This paper describes the work on mining the suggestions from online reviews and forums. Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice. The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (suggestion mining) uses a rule-based approach for feature selection, SMOTE technique for data augmentation and deep learning technique (Convolutional Neural Network) for classification. We have compared the results with Random Forest classifier (RF) and Multi-Layer Perceptron (MLP) model. Results show that the CNN model performs better than other models for both the subtasks.

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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 3: Irony Detection in English Tweets Using MultiLayer Perceptron

Cited by 4 publications

References 9 publications

Ensemble-based domain adaptation on social media posts for irony detection

Ensemble-based domain adaptation on social media posts for irony detection

SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data

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

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