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.
In the end, it is clear that an unsupervised model for text representation is needed to give robust topic-independent text representations. One of the most successful and widely used text representation models is Word2Vec, which has proven to give good results regardless of the language in opinion mining and topic detection duties. For instance, @cite_41 combined Word2Vec and a bag-of-words document classifier, and showed that Word2Vec provided word embeddings that produced more stable results when doing cross-domain classification experiments. Also, since Word2Vec was first introduced, there has been some research trying to improve and fine-tune word embeddings. Such is the case of @cite_6 that proposes a hybrid model between skip-gram model and continuous bag of words (CBOW) called mixed word embedding (MWE). All in all, we choose word embedding models such as Word2Vec for being able to embed semantic similarities between words in a similarity metric defined over a Euclidean vector space.
@cite_41 @cite_6
2509219942 2404244834
Vector-based word representations can help to improve a document classifier.The information of word2vec vectors and bags of words are very complementary.The combination of word2vec and BOW word representations obtains the best results.Word2vec is much more stable than bag of words models in cross-domain experiments. In this paper we show how a vector-based word representation obtained via word2vec can help to improve the results of a document classifier based on bags of words. Both models allow obtaining numeric representations from texts, but they do it very differently. The bag of words model can represent documents by means of widely dispersed vectors in which the indices are words or groups of words. word2vec generates word level representations building vectors that are much more compact, where indices implicitly contain information about the context of word occurrences. Bags of words are very effective for document classification and in our experiments no representation using only word2vec vectors is able to improve their results. However, this does not mean that the information provided by word2vec is not useful for the classification task. When this information is used in combination with the bags of words, the results are improved, showing its complementarity and its contribution to the task. We have also performed cross-domain experiments in which word2vec has shown much more stable behavior than bag of words models. Learning distributed word representations has been a popular method for various natural language processing applications such as word analogy and similarity, document classification and sentiment analysis. However, most existing word embedding models only exploit a shallow slide window as the context to predict the target word. Because the semantic of each word is also influenced by its global context, as the distributional models usually induced the word representations from the global co-occurrence matrix, the window-based models are insufficient to capture semantic knowledge. In this paper, we propose a novel hybrid model called mixed word embedding (MWE) based on the well-known word2vec toolbox. Specifically, the proposed MWE model combines the two variants of word2vec, i.e., SKIP-GRAM and CBOW, in a seamless way via sharing a common encoding structure, which is able to capture the syntax information of words more accurately. Furthermore, it incorporates a global text vector into the CBOW variant so as to capture more semantic information. Our MWE preserves the same time complexity as the SKIP-GRAM. To evaluate our MWE model efficiently and adaptively, we study our model on linguistic and application perspectives with both English and Chinese dataset. For linguistics, we conduct empirical studies on word analogies and similarities. The learned latent representations on both document classification and sentiment analysis are considered for application point of view of this work. The experimental results show that our MWE model is very competitive in all tasks as compared with the state-of-the-art word embedding models such as CBOW, SKIP-GRAM, and GloVe.