Making Sense of Large Volumes of Unstructured Responses K. M. P. N. Jayathilaka Department of Statistics University of Colombo

Outline  Introduction  Objectives  Implementation  Results  Conclusions

Introduction  Big Data Analytics  Topic Modeling  Sentiment Analysis

Big Data Analytics  In the modern world, big data analytics becomes a key component that is helpful for decision making and identifying hidden patterns of data  Big data analytics concept helps to get ideas for analyzing large data sets

Topic Modeling  Topic modeling is a statistical model used to discover "topics" in collection of unstructured textual documents  Topic is a key idea used to represent the content of documents  The Latent Dirichlet Allocation (LDA) topic model is frequently used for text analytics of set of documents

Sentiment Analysis  Sentiment analysis is the technique used to classify the polarity of text data  The polarity of text data can be considered as positive, negative and neutral

Sentiment Analysis  Lexicon-based approach is the unsupervised technique used for sentiment analysis  The lexicon-based technique takes less time for the classification and it does not require a training set Sentiment Analysis Machine Learning Approach Lexicon-Based Approach Supervised Learning Unsupervised Learning Dictionary-Based Approach Corpus-Based Approach

Objectives  Making sense of large collections of unstructured responses o To identify the nature of the data to take ideas for analyzing data o To handle the large volume of dataset o To identify key ideas of Indian Internet users about Net Neutrality using topic model o To classify the polarity of responses using sentiment analysis

Implementation  Data  Data Preprocessing  Topic Modeling  Sentiment Analysis

Data  The data set included about 500,000 submissions received in response to a debate on Net Neutrality in India  There were two types of responses o Separate answers to each twenty question asked from Telecom Regulatory Authority of India o General comments of internet users in India

Examples of Data Fragment of a Question Based Response

Examples of Data Example of a Comment

Data Preprocessing  Extracting plaintext from HTML files and cleaning data  Extracting answers for questions  Data formatting o Stop word removal o Lemmatizing

Technology Used for Data Preprocessing and Analyzing  Python  R  Natural Language Toolkit (NLTK)

Topic Modeling  Determining the number of topics o The number of topics was determined by examining the topic models which were fitted in each question  Evaluating the generated topics  Visualizing the topics

Sentiment Analysis  Sentiment analysis using Multi-perspective Question Answering lexicon resource  Sentiment analysis using SentiWordNet lexicon resource

Results Topic Modeling Topic 1Topic 2Topic 3 WeightWordWeightWordWeightWord 0.024law0.036increase0.009speed 0.019time0.024account0.008neutrality 0.016act0.024lead0.008net 0.015telecom0.024cost0.007ott 0.012digital0.013complexity0.006penetration 0.012consultation0.013financial0.006establish 0.012application0.013accessible0.006evolving 0.011information0.012degradation0.005high 0.010subject0.011involve0.004made 0.010indian0.010essential0.004early LDA Topic Model

Results Topic Modeling Word Cloud

Results Sentiment Analysis  The lexicon-based approach to sentiment detection reveals that most of the responses are positive

Conclusions  Identifying general ideas of Indian Internet users about Net Neutrality by carrying out analytics on this large data set  The topics generated from the LDA model are mainly focused on Internet problems and Net Neutrality  There were key issues such as regulations of OTT players, Internet security and privacy, speed of Internet services  The results of the lexicon-based approach to sentiment detection reveals that most of the responses are positive

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