Sentiment analysis in geo social streams by using machine learning technique

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesMassive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiments from text. The rapid increase in the volume of sentiment rich data on the web has resulted in an increased interaction among researchers regarding sentiment analysis and opinion (Kaushik & Mishra, 2014). However, limited research has been conducted considering location as another dimension along with the sentiment rich data. In this work, we analyze the sentiments of Geotweets, tweets containing latitude and longitude coordinates, and visualize the results in the form of a map in real time. We collect tweets from Twitter using its Streaming API, filtered by English language and location (bounding box). For those tweets which don’t have geographic coordinates, we geocode them using geocoder from GeoPy. Textblob, an open source library in python was used to calculate the sentiments of Geotweets. Map visualization was implemented using Leaflet. Plugins for clusters, heat maps and real-time have been used in this visualization. The visualization gives an insight of location sentiments

Feat: A Facebook Extraction And Analysis Toolkit

Social media usage has become mainstream. According to a recent study done by Edison Research in 2016, 78% of the U.S. population has a social media profile [8]. The number of active Facebook users is over one billion. In addition, 71% of adults use Facebook, which is the target of this thesis. Because Facebook is so widely used, it is also a popular medium for those wanting to promote their products and ideas, including presidential candidates. Many researchers have extracted data from social media sites, including Facebook, to predict the outcome of elections, to predict election turnout by political party, and to determine voter opinions. This thesis will discuss the development and use of a suite of tools for gathering and analyzing data collected from the social media site, Facebook. Although the suite of tools can be used to collect data from any public Facebook site, this thesis will specifically focus on using the tools to extract data from the pages of presidential candidates. In addition to extracting Facebook data and storing the data in a database, tools in the suite can be used to analyze and visualize the collected data

Partisanship in Crisis: Public Response to Covid-19 Pandemic in Indonesia

Given the fact that in a context of crises, people are concerned with their safety, among other things, partisan response toward policies and public leaders is an intriguing topic. This article examines the extent to which partisanship pertains to the time of the Covid-19 pandemic. We employ natural language processing (NLP) and social network analysis (SNA) on Twitter data to analyse public responses toward prominent political leaders, namely, Indonesian President Joko Widodo (Jokowi) and Jakarta Governor Anies Baswedan (Anies), in handling the crisis of the Covid-19 pandemic in Indonesia. We then put the social media analysis in a framework of political partisanship. Our sentiment analysis through NLP across time and categories found that supports and demands towards the two public figures indicate positive and negative partisanship that replicates previous electoral supports. Similarly, our SNA indicates a high polarization rate among the accounts connected with the two leaders in response to the crisis. Extended analysis of the accounts who are at the epicentres of the sentiment conversations, either positive or negative about Jokowi and Anies, reveals that there are connections with their past political support. Though we find negative partisan responses for both leaders, a type of hard-core partisanship has been leveraged for Jokowi but not for Anies. We conclude that electoral polarization contributes to the extent to which partisanship responses circulate in a context of crisis

QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns

Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.Comment: To appear in the Proceedings of WWW 2015. 11pp, 10 fig. Interactive visualization, data, and other info available at http://snap.stanford.edu/quotus

TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied