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.
It is worth noting that the majority of work has been done using English text corpora with valuable results @cite_15 . However, Spanish is significantly a more inflected language than English, and this difference could pose problems. For example, supervised machine learning methods for the topic classification of annotated Spanish tweets modeled with @math -grams have shown to be insufficient @cite_28 . Better attempts to deal with the several research branches of topic detection and opinion-mining in Spanish have taken place. For instance, in the field of opinion mining, studies like @cite_33 proposed a lexicon-based model that adapts to specific domains in Spanish for polarity classification of film reviews. Also, polarity classification in Spanish tweets has been treated in @cite_24 , where hybrid systems that bring together knowledge from lexical, syntactic and semantic structures in the Spanish language, as well as machine learning techniques used with the bag-of-words representation, have shown improvements over the sole bag of words approach. Besides, the clever creation of a corpus called MeSiento (Spanish for I feel'') @cite_26 allowed a robust unsupervised method for polarity classification of Spanish tweets that reached accuracy levels close to the ones obtained with supervised algorithms.
@cite_26 @cite_33 @cite_28 @cite_24 @cite_15
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This work presents a novel method for the generation of a knowledge base oriented to Sentiment Analysis from the continuous stream of published micro-blogs in social media services like Twitter. The method is simple in its approach and has shown to be effective compared to other knowledge based methods for Polarity Classification. Due to independence from language, the method has been tested on different Spanish corpora, with a minimal effort in the lexical resources involved. Although for two of the three studied corpora the obtained results did not improve those officially obtained on the same corpora, it should be noted that this is an unsupervised approach and the accuracy levels achieved were close to those levels obtained with well-known supervised algorithms. A lexicon-based domain adaptation method is proposed.Several domain polar lexicons were compiled following a corpus-based approach.The new resources are assessed over a Spanish corpus.The promising results encourage us to follow improving this domain adaptation method. One of the problems of opinion mining is the domain adaptation of the sentiment classifiers. There are several approaches to tackling this problem. One of these is the integration of a list of opinion bearing words for the specific domain. This paper presents the generation of several resources for domain adaptation to polarity detection. On the other hand, the lack of resources in languages different from English has orientated our work towards developing sentiment lexicons for polarity classifiers in Spanish. The results show the validity of the new sentiment lexicons, which can be used as part of a polarity classifier. A signicant amount of eort is been invested in constructing eective solutions for sentiment analysis and topic detection, but mostly for English texts. Using a corpus of Spanish tweets, we present a comparative analysis of dierent approaches and classication We describe a system that classifies the polarity of Spanish tweets. We adopt a hybrid approach, which combines machine learning and linguistic knowledge acquired by means of NLP. We use part-of-speech tags, syntactic dependencies and semantic knowledge as features for a supervised classifier. Lexical particularities of the language used in Twitter are taken into account in a pre-processing step. Experimental results improve over those of pure machine learning approaches and confirm the practical utility of the proposal. With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.