Abstract

Sexism is prevalent in today’s society, both offline and online, and poses a credible threat to social equality with respect to gender. According to ambivalent sexism theory (Glick and Fiske, 1996), it comes in two forms: Hostile and Benevolent. While hostile sexism is characterized by an explicitly negative attitude, benevolent sexism is more subtle. Previous works on computationally detecting sexism present online are restricted to identifying the hostile form. Our objective is to investigate the less pronounced form of sexism demonstrated online. We achieve this by creating and analyzing a dataset of tweets that exhibit benevolent sexism. By using Support Vector Machines (SVM), sequence-to-sequence models and FastText classifier, we classify tweets into ‘Hostile’,‘Benevolent’or ‘Others’ class depending on the kind of sexism they exhibit. We have been able to achieve an F1-score of 87.22% using FastText classifier. Our work helps analyze and understand the much prevalent ambivalent sexism in social media.

Abstract

This work presents a corpus of Bollywood song lyrics and its metadata, annotated with sentiment polarity. We call this BolLy. It contains lyrics of 1055 songs ranging from those composed in the year 1970 to the most recent ones. This dataset is of utmost value as all the annotation is done manually by three annotators and this makes it a very rich dataset for training purposes. In this work, we describe the creation and annotation process, content, and the possible uses of the dataset. As an experiment, we have built a basic classification system to identify the emotion polarity of the song based solely on the lyrics and this can be used as a baseline algorithm for the same. BolLy can also be used for studying code-mixing with respect to lyrics.

Abstract

Sentiment Analysis is one of the most active research areas in natural language processing and an extensively studied problem in data mining, web mining and text mining for English language. With the proliferation of social media these days, data is widely increasing in regional languages along with English. Telugu is one such regional language with abundant data available in social media, but it’s hard to find a labeled training set as human annotation is time-consuming and cost-ineffective. To address this issue, in this paper the practicality of active learning for Telugu sentiment analysis is investigated. We built a hybrid approach by combining different query selection strategy frameworks to increase more accurate training data instances with limited labeled data. Using a set of classifiers like SVM, XGBoost, and Gradient Boosted Trees (GBT), we achieved promising results with minimal error rate.

Abstract

This paper describes an end to end dialog system created using sequence to sequence learning and memory networks for Telugu, a low-resource language. We automatically generate dialog data for Telugu in the tourist domain, using a knowledge base that provides tourist place, type, tour time, etc. Using this data, we train a sequence to sequence model to learn system responses in the dialog. In order to add the query prediction for information retrieval (through API calls), we train a memory network. We also handle cases requiring updation of API calls and querying for additional information. Using the combination of sequence to sequence learning and memory network, we successfully create an end to end dialog system for Telugu.

Abstract

Sentiment analysis deals with the task of determining the polarity of a document or sentence and has received a lot of attention in recent years for the English language. With the rapid growth of social media these days, a lot of data is available in regional languages besides English. Telugu is one such regional language with abundant data available in social media, but it’s hard to find a labelled data of sentences for Telugu Sentiment Analysis. In this paper, we describe an effort to build a gold-standard annotated corpus of Telugu sentences to support Telugu Sentiment Analysis. The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then pre-processed and manually annotated by native Telugu speakers using our annotation guidelines. In total, we have annotated 5457 sentences, which makes our corpus the largest resource currently available. The corpus and the annotation guidelines are made publicly available.

Abstract

Word embeddings are being used for several linguistic problems and NLP tasks. Improvements in solutions to such problems are great because of the recent breakthroughs in vector representation of words and research in vector space models. However, vector embeddings of phrases keeping semantics intact with words has been challenging. We propose a novel methodology using Siamese deep neural networks to embed multi-word units and fine-tune the current state-of-the-art word embeddings keeping both in the same vector space. We show several semantic relations between words and phrases using the embeddings generated by our system and evaluate that the similarity of words and their corresponding paraphrases are maximized using the modified embeddings.

Abstract

As a discipline of Natural Language Processing, Sentiment Analysis is used to extract and analyze subjective information present in natural language data. The task of Sentiment Analysis has acquired wide commercial uses including social media monitoring tasks, survey responses, review systems, etc. Languages like English have several resources which aid in the task of Sentiment Analysis. SentiWord-Net and Subjectivity WordList are examples of such tools and resources. With more data being available in native vernacular, language-specific SentiWordNet (s) have become essential. For resource poor languages, creating such SentiWordNet (s) is a difficult task to achieve. One solution is to use available resources in English and translate the final source lexicon to target lexicon via machine translation. Machine translation systems for the English-Odia language pair have not yet been developed. In this paper, we discuss a method to create a SentiWordNet for Odia, which is resource-poor, by only using resources which are currently available for Indian languages. The lexicon created, would serve as a tool for Sentiment Analysis related task specific to Odia data.

Abstract

The main focus of this paper is towards the development of a large vocabulary Telugu speech database. Telugu is a low resource language where there exists no standardized database for building the speech recognition system (ASR). The database consists of neutral speech samples collected from 100 speakers for building the Telugu ASR system and it was named as IIIT-H Telugu speech corpus. The speech and text corpus design and the procedure followed for the collection of the database have been discussed in detail. The preliminary ASR system results for the models built in this database are reported. The architectural choices of deep neural networks (DNNs) play a crucial role in improving the performance of ASR systems. ASR trained with hybrid DNNs (DNNHMM) with more hidden layers have shown better performance over the conventional GMMs (GMM-HMM). Kaldi tool kit is used for building the acoustic models required for the ASR system.

Abstract

Transfer learning algorithms can be used when sufficient amount of training data is available in the source domain and limited training data is available in the target domain. The transfer of knowledge from one domain to another requires similarity between two domains. In many resource-poor languages, it is rare to find labeled training data in both the source and target domains. Active learning algorithms, which query more labels from an oracle, can be used effectively in training the source domain when an oracle is available in the source domain but not available in the target domain. Active learning strategies are subjective as they are designed by humans. It can be time consuming to design a strategy and it can vary from one human to other. To tackle all these problems, we design a learning algorithm that connects transfer learning and active learning with the well-known multi-armed bandit problem by querying …

Abstract

The furtherance of social media led people to share the reviews in various ways such as video, audio and text. Recently, the performance of sentiment classification is achieved success using neural networks. In this paper, neural network approach is presented to detect the sentiment from audio and text models. For audio, features like Mel Frequency Cepstral Coefficients (MFCC) are used to build Deep Neural Network (DNN) and Deep Neural Network Attention Mechanism (DNNAM) classifiers. From the results, it is noticed that DNNAM gives better results compared to DNN because the DNN is a frame based one where as the DNNAM is an utterance level classification thereby efficiently use the context. Additionally, textual features are extracted from the transcript of the audio input using Word2vec model. Support Vector Machine (SVM) and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) classifiers are used to develop a sentiment model. From the experiments it is noticed the LSTM-RNN outperforms the SVM as the LSTM-RNN is able to memorize long temporal context. The performance is also significantly improved by combining both the audio and text modalities.