Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201

Implementation of Classification of Geolocation of Country from Worldwide Tweets

Social media are progressively being employed within the scientific community as key supply of knowledge to assist perceive various natural and social phenomena, and this has prompted the event of a good vary of process data processing tools that may extract data from social media for each post-hoc and real time analysis. The rise of interest in mistreatment social media as a supply for analysis has actuated braving the challenge of mechanically geo-locating tweets, given the dearth of specific location data within the majority of tweets. In distinction to abundant previous work that has targeted on location classification of tweets restricted to a selected country, here we tend to undertake the task during a broader context by classifying international tweets at the country level that is up to now undiscovered during a time period situation. We tend to analyze the extent to that a tweet’s country of origin maybe determined by creating use of eight tweet-inherent options for classification