Ranking and Extraction of Relevant Single Words in Text

Ventura, João; Silva, Joaquim Ferreira da

doi:10.5772/6035

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In fact, in the literature, a wide range of techniques from areas such as statistics and information theory has been used to analyze a variety of discrete data such as written texts (including web pages), computer assembly code, and biological sequences (Berryman et al ). Concerning text analysis, most of the current information‐retrieval methods rely on the extraction of keywords (Krestel & Fankhauser ; Ventura and Ferreira da Silva ). This technique is used in several applications including the characterization of document topics.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Words Associations in Texts Based on Joint Distribution of Words Occurrences

Santoni

Pourabbas

2015

Computational Intelligence

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In this article, we propose a novel approach for measuring word association based on the joint occurrences distribution in a text. Our approach relies on computing a sum of distances between neighboring occurrences of a given word pair and comparing it with a vector of randomly generated occurrences. The idea behind this assumption is that if the distribution of co-occurrences is close to random or if they tend to appear together less frequently than by chance, such words are not semantically related. We devise a distance function S that evaluates the words association rate. Using S, we build a concept tree, which provides a visual and comprehensive representation of keywords association in a text. In order to illustrate the effectiveness of our algorithm, we apply it to three different texts, showing the consistency and significance of the obtained results with respect to the semantics of documents. Finally, we compare the results obtained by applying our proposed algorithm with the ones achieved by both human experts and the co-occurrence correlation method. We show that our method is consistent with the experts' evaluation and outperforms with respect to the co-occurrence correlation method.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Words Associations in Texts Based on Joint Distribution of Words Occurrences

Santoni

Pourabbas

2015

Computational Intelligence

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“…There is plenty of room for improvement in the natural language processing part of our study -other, more elaborate methods could be used instead of the simple Bag-of-Words for extracting word information from text. Ventura and Ferreira da Silva (2008) give an overview of the state-of-the-art methods that are in use nowadays.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

UsingWords from Daily News Headlines to Predict the Movement of Stock Market Indices

Kavšek¹

2017

MGT

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Stock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation. In this paper, we propose a more descriptive approach focusing on the models themselves, trying to identify the individual words in the text that most affect the movement of stock market indices. We use data from two sources (for the past eight years): the daily data for the Dow Jones Industrial Average index ('open' and 'close' values for each trading day) and the headlines of the most voted 25 news on the Reddit WorldNews Channel for the previous 'trading days. ' By applying machine learning algorithms on these data and analysing individual words that appear in the final predictive models, we find that the words gay, propaganda and massacre are typically associated with a daily increase of the stock index, while the word iran mostly coincide with its decrease. While this work presents a first step towards qualitative analysis of stock market models, there is still plenty of room for improvements.

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