2,630 research outputs found
Visualizing and Quantifying Impact and Effect in Twitter Narrative using Geometric Data Analysis
We use geometric multivariate data analysis which has been termed a
methodology for both the visualization and verbalization of data. The general
objectives are data mining and knowledge discovery. In the first case study, we
use the narrative surrounding very highly profiled tweets, and thus a Twitter
event of significance and importance. In the second case study, we use eight
carefully planned Twitter campaigns relating to environmental issues. The aim
of these campaigns was to increase environmental awareness and behaviour.
Unlike current marketing, political and other communication campaigns using
Twitter, we develop an innovative approach to measuring bevavioural change. We
show also how we can assess statistical significance of social media behaviour.Comment: 34 pages, 11 figure
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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
Sentiment analysis of text with lossless mining
Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier
- …