Discovering conversational topics and emotions associated with Demonetization tweets in India
نویسندگان
چکیده
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.
منابع مشابه
An Empirical Insight of Examining Impact of Recent Demonetization on Monetary System: Evidence from India
Abstract D emonetization initiative by Govt. of India in Nov-Dec, 2016 aimed at addressing the issues like black money, hoarding and overall cleansing the monetary system. This paper in this regard attempts to empirically examine the impact of demonetization drive upon the monetary system by taking data of 180 days prior to Nov, 2016. The cointegration results exhibit show a long run ...
متن کاملDiscovering Emotions in the Wild: An Inductive Method to Identify Fine-grained Emotion Categories in Tweets
This paper describes a method to expose a set of categories that are representative of the emotions expressed on Twitter inductively from data. The method can be used to expand the range of emotions that automatic classifiers can detect through the identification of fine-grained emotion categories human annotators are capable of detecting in tweets. The inter-annotator reliability statistics fo...
متن کاملModeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter
Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This paper introduces a fr...
متن کاملIdentifying Health-Related Topics on Twitter - An Exploration of Tobacco-Related Tweets as a Test Topic
Public health-related topics are difficult to identify in large conversational datasets like Twitter. This study examines how to model and discover public health topics and themes in tweets. Tobacco use is chosen as a test case to demonstrate the effectiveness of topic modeling via LDA across a large, representational dataset from the United States, as well as across a smaller subset that was s...
متن کاملDo You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations
We propose a computational framework for analyzing the social aspects of sentiments and emotions in Twitter conversations. We explore the question of sentiment and emotion transitions, asking the question do you feel what I feel? in a conversation. We also inquire whether conversational partners can influence each other, altering their sentiments and emotions, and if so, how they can do so. Fur...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1711.04115 شماره
صفحات -
تاریخ انتشار 2017