Shad Akhtar

Feature selection and ensemble construction

In this paper we present a cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis. Aspect based sentiment analysis is performed in two steps, viz. aspect term extraction and sentiment classification. The pruned, compact set of features performs better compared to the baseline model that makes use of the complete set of features for aspect term extraction and sentiment classification. We further construct an ensemble based on PSO, and put it in cascade after the feature selection module. We use the features that are identified based on the properties of different classifiers and domains. As base learning algorithms we use three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Experiments for aspect term extraction and sentiment analysis on two different kinds of domains show the effectiveness of our proposed approach.

IITP: Multiobjective Differential Evolution based Twitter Named Entity Recognition

In this paper we propose a differential evolution (DE) based named entity recognition (NER) system in twitter data. In the first step, we develop various NER systems using different combinations of the features. We implemented these features without using any domain-specific features and/or resources. As a base classifier we use Conditional Random Field (CRF). In the second step, we propose a DE based feature selection approach to determine the most relevant set of features and its context information. The optimized feature set applied to the training set yields the precision, recall and Fmeasure values of 60.68%, 29.65% and 39.84%, respectively for the fine-grained named entity (NE) types. When we consider only the coarse-grained NE types, it shows the precision, recall and F-measure values of 63.43%, 51.44% and 56.81%, respectively.

IITP: Hybrid Approach for Text Normalization in Twitter

In this paper we report our work for normalization of noisy text in Twitter data. The method we propose is hybrid in nature that combines machine learning with rules. In the first step, supervised approach based on conditional random field is developed, and in the second step a set of heuristics rules is applied to the candidate wordforms for the normalization. The classifier is trained with a set of features which were are derived without the use of any domain-specific feature and/or resource. The overall system yields the precision, recall and F-measure values of 90.26%, 71.91% and 80.05% respectively for the test dataset.

A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis

In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These are trained on top of pre-trained, autoencoder-based, financial word embeddings and lexicon features. An ensemble is constructed by combining these deep learning models and a classical supervised model based on Support Vector Regression (SVR). We evaluate our proposed technique on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The propose model shows impressive results on two datasets, i.e. microblogs and news headlines datasets. Comparisons show that our proposed model performs better than the existing state-of-the-art systems for the above two datasets by 2.0 and 4.1 cosine points, respectively.

Feature Selection Using Multi-objective Optimization for Aspect Based Sentiment Analysis

In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.

Aspect Based Sentiment Analysis: Category Detection and Sentiment Classification for Hindi

E-commerce markets in developing countries (e.g. India) have witnessed a tremendous amount of user’s interest recently. Product reviews are now being generated daily in huge amount. Classifying the sentiment expressed in a user generated text/review into certain categories of interest, for example, positive or negative is famously known as sentiment analysis. Whereas aspect based sentiment analysis (ABSA) deals with the sentiment classification of a review towards some aspects or attributes or features. In this paper we asses the challenges and provide a benchmark setup for aspect category detection and sentiment classification for Hindi. Aspect category can be seen as the generalization of various aspects that are discussed in a review. As far as our knowledge is concerned, this is the very first attempt for such kind of task involving any Indian language. The key contributions of the present work are two-fold, viz. providing a benchmark platform by creating annotated dataset for aspect category detection and sentiment classification, and developing supervised approaches for these two tasks that can be treated as a baseline model for further research.