Abstract With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts. This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogota’s citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months. Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics.
Nonetheless, these methods applied to sentiment analysis tasks depend a lot on annotated dictionaries which do not contain Bogotá's jargon. As a matter of fact, few attempts have been made to make small topic-specific dictionaries such as @cite_43 , where the political sentiment towards Bogotá mayoral candidates for 2015 was analyzed using Twitter and a political sentiment dictionary defined in the Colombian political context. A second study briefly examined the sentiment of tweets from Bogotá with words related with health symptoms @cite_19 . A third study examined the results of 2015 Colombian regional elections and compared them with political ideology and Twitter activity of the candidates @cite_16 .
@cite_19 @cite_43 @cite_16
2184009014 2767058267 2060968456 2156413587
We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections. Efforts to assess people’s sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people’s attitudes toward products, people, or topics and their attributes aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis’s Twitris system. This paper analyzes a corpus of Nigerian Tweets collected during the run-up to the 2011 Nigerian Presidential election and compares it with official election returns and polling data. We found that counts of the mentions on Twitter of the two major candidates correlated strongly with polling and election results when compared across the country's geopolitical regions, though the same data over represented two other candidates who did not fare as well in the polls or at the ballot box. Sentiment extracted from Twitter was less accurate in capturing mean levels of support for the two major candidates and, in particular, showed a strong negativity bias against the incumbent president. Twitter sentiment did mirror regional trends in the polling and election results though not as strongly as seen for mention counts. We demonstrate methodologically how to sample these data, extract sentiment, and compare this sentiment with ground-truth polling data and election results. Although social media clearly capture opinion about contentious issues, our results suggest that the opinion represented there may not always accurately reflect true public opinion. We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task 'Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.