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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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. 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. We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments. We present a semi-supervised approach to the problem of paradigm induction from inflection tables. Our system extracts generalizations from inflection tables, representing the resulting paradigms in an abstract form. The process is intended to be language-independent, and to provide human-readable generalizations of paradigms. The tools we provide can be used by linguists for the rapid creation of lexical resources. We evaluate the system through an inflection table reconstruction task using Wiktionary data for German, Spanish, and Finnish. With no additional corpus information available, the evaluation yields per word form accuracy scores on inflecting unseen base forms in different languages ranging from 87.81 (German nouns) to 99.52 (Spanish verbs); with additional unlabeled text corpora available for training the scores range from 91.81 (German nouns) to 99.58 (Spanish verbs). We separately evaluate the system in a simulated task of Swedish lexicon creation, and show that on the basis of a small number of inflection tables, the system can accurately collect from a list of noun forms a lexicon with inflection information ranging from 100.0 correct (collect 100 words), to 96.4 correct (collect 1000 words).