Open AccessAn approach for suggestion mining based on deep learning techniques
Author(s) -
T. Jaya Venkata Rama Reddy,
P. Vijayapal Reddy,
T. Murali Mohan,
Raju Dara
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1074/1/012021
Subject(s) - computer science , sentiment analysis , artificial intelligence , deep learning , domain (mathematical analysis) , machine learning , natural language processing , competition (biology) , product (mathematics) , data science , mathematical analysis , ecology , geometry , mathematics , biology
An organization often uses forums and social media channels for getting feedback from costumers or users. The ratings of products on rating platforms are a useful feedback to make a product better. The feedback from a customer is in the form of a suggestion which appears in a rating text or is directly asked from the customer. Suggestion mining is a binary classification problem that labels sentences as Suggestion or Non-suggestion. The suggestion mining is similar to sentiment analysis which is associated with common linguistic properties and challenges irrespective of the domain and application. Most of the previous works of suggestion mining proposed rule based methods and a very few developed statistical classifiers by using manually identified features. Recently, several researchers paid attention on deep learning technique based solutions to suggestion mining where features are automatically learned. In this work, various deep learning techniques like RNN, LSTM, Attention based LSTM and GRU are used in the experimentation of suggestion mining. The experiment carried out on the dataset provided in SemEval 2019 suggestion mining competition. The Attention based LSTM achieved best accuracies for suggestion mining when compared with other deep learning techniques.