Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

Sentiment analysis on Twitter

In recent years more and more people have been connecting with Social Networks. One of the most used is Twitter. This huge amount of information is attracting the interest of companies. One reason is that this huge source of information can be used to detect public opinion about their brands and thus improve their business values. In order to transform the information present in the Social Networks into knowledge several steps are required. This project aim to describe them and provide tools that are able to perform this task. The first problem is how to retrieve the data. Several ways are available, each one with its own pros and cons. After that it is necessary to study and define proper queries in order to retrieve the information needed. Once the data is retrieved you may need to filter and explore your data. For this task a Topic Model Algorithm ( LDA ) has been studied and analyzed. LDA has shown positive results when it is tuned in the proper way and it is combined with appropriate visualization techniques. The difference between a Topic Model Algorithm and other Clustering/Segmentation techniques is that Topic Models allows each ”document” ( instance ) to belong to more than one topic ( cluster ). LDA doesn’t natively work well on Twitter due to the very short length of the tweets. An investigation in the literature has revealed a solution to this problem. Another problem that is common in clustering is how to validate the Algorithm and how to choose the proper number of topics ( clusters), for this problem several metrics in the literature have been explored. Afterwards, Sentiment Analysis techniques can be applied in order to measure the opinion of the users . The literature presents several approaches and ways to solving this problem. This work is focused in solving the Polarity Detection task, with three classes , so, classify if a tweet express a positive , a negative or a neutral sentiment. Here reach accurate results can be challenging, due to the messy nature of the twitter posts. Several approaches have been tested and compared. The baseline method tested is the use of sentiment dictionaries, after that , since the real sentiment of the twitter posts is not available, a sample has been manually labeled and several Supervised approaches combined with various Feature Selection/Transformation techniques have been tested. Finally, a totally new experimental approach, inspired from the Soft Labeling technique present in the literature, has been defined and tested. This method try to avoid the costly task to manually label a sample in order to validate a model. In the literature this problem is solved for the two-class problem, so by considering only positive and negative tweets. This work try to extend the soft-labeling approach to the three class problem

A hierarchical topic modelling approach for tweet clustering

While social media platforms such as Twitter can provide rich and up-to-date information for a wide range of applications, manually digesting such large volumes of data is difficult and costly. Therefore it is important to automatically infer coherent and discriminative topics from tweets. Conventional topic models and document clustering approaches fail to achieve good results due to the noisy and sparse nature of tweets. In this paper, we explore various ways of tackling this challenge and finally propose a two-stage hierarchical topic modelling system that is efficient and effective in alleviating the data sparsity problem. We present an extensive evaluation on two datasets, and report our proposed system achieving the best performance in both document clustering performance and topic coherence

What you say and how you say it : joint modeling of topics and discourse in microblog conversations

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier