Rama Rohit Reddy Gangula


2020

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Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language
Yashwanth Reddy Regatte | Rama Rohit Reddy Gangula | Radhika Mamidi
Proceedings of the Twelfth Language Resources and Evaluation Conference

In recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the internet. It has various applications like opinion mining, social media monitoring, and market research. Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sentiment expressed. Significant contributions have been made in ABSA, but this progress is limited only to a few languages with adequate resources. Telugu lags behind in this area of research despite being one of the most spoken languages in India and an enormous amount of data being created each day. In this paper, we create a reliable resource for aspect based sentiment analysis in Telugu. The data is annotated for three tasks namely Aspect Term Extraction, Aspect Polarity Classification and Aspect Categorisation. Further, we develop baselines for the tasks using deep learning methods demonstrating the reliability and usefulness of the resource.

2019

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Detecting Political Bias in News Articles Using Headline Attention
Rama Rohit Reddy Gangula | Suma Reddy Duggenpudi | Radhika Mamidi
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Language is a powerful tool which can be used to state the facts as well as express our views and perceptions. Most of the times, we find a subtle bias towards or against someone or something. When it comes to politics, media houses and journalists are known to create bias by shrewd means such as misinterpreting reality and distorting viewpoints towards some parties. This misinterpretation on a large scale can lead to the production of biased news and conspiracy theories. Automating bias detection in newspaper articles could be a good challenge for research in NLP. We proposed a headline attention network for this bias detection. Our model has two distinctive characteristics: (i) it has a structure that mirrors a person’s way of reading a news article (ii) it has attention mechanism applied on the article based on its headline, enabling it to attend to more critical content to predict bias. As the required datasets were not available, we created a dataset comprising of 1329 news articles collected from various Telugu newspapers and marked them for bias towards a particular political party. The experiments conducted on it demonstrated that our model outperforms various baseline methods by a substantial margin.

2018

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Predicting the Genre and Rating of a Movie Based on its Synopsis
Varshit Battu | Vishal Batchu | Rama Rohit Reddy Gangula | Mohana Murali Krishna Reddy Dakannagari | Radhika Mamidi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction
Rama Rohit Reddy Gangula | Radhika Mamidi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)