Entity extraction e text classification come strumenti per la sentiment analysis di testi giornalistici

In this thesis we are going to talk about technologies which allow us to approach sentiment analysis on newspapers articles. The final goal of this work is to help social scholars to do content analysis on big corpora of texts in a faster way thanks to the support of automatic text classification.

Protein-protein interactions classification from text via local learning with class priors

Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.

Using Web Search Logs to Identify Query Classification Terms

Purpose – The work presented in this paper aims to provide an approach to classifying web logs by personal properties of users. Design/methodology/approach – The authors describe an iterative system that begins with a small set of manually labeled terms, which are used to label queries from the log. A set of background knowledge related to these labeled queries is acquired by combining web search results on these queries. This background set is used to obtain many terms that are related to the classification task. The system then ranks each of the related terms, choosing those that most fit the personal properties of the users. These terms are then used to begin the next iteration. Findings – The authors identify the difficulties of classifying web logs, by approaching this problem from a machine learning perspective. By applying the approach developed, the authors are able to show that many queries in a large query log can be classified. Research limitations/implications – Testing results in this type of classification work is difficult, as the true personal properties of web users are unknown. Evaluation of the classification results in terms of the comparison of classified queries to well known age-related sites is a direction that is currently being exploring. Practical implications – This research is background work that can be incorporated in search engines or other web-based applications, to help marketing companies and advertisers. Originality/value – This research enhances the current state of knowledge in short-text classification and query log learning. Classification schemes, Computer networks, Information retrieval, Man-machine systems, User interfaces

Scoring-thresholding pattern based text classifier

A big challenge for classification on text is the noisy of text data. It makes classification quality low. Many classification process can be divided into two sequential steps scoring and threshold setting (thresholding). Therefore to deal with noisy data problem, it is important to describe positive feature effectively scoring and to set a suitable threshold. Most existing text classifiers do not concentrate on these two jobs. In this paper, we propose a novel text classifier with pattern-based scoring that describe positive feature effectively, followed by threshold setting. The thresholding is based on score of training set, make it is simple to implement in other scoring methods. Experiment shows that our pattern-based classifier is promising.

A pattern based two-stage text classifier

In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.

Relevance feature discovery for text mining

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.

Efficient classification using phrases generated by topic models

There are many popular models available for classification of documents like Naïve Bayes Classifier, k-Nearest Neighbors and Support Vector Machine. In all these cases, the representation is based on the “Bag of words” model. This model doesn't capture the actual semantic meaning of a word in a particular document. Semantics are better captured by proximity of words and their occurrence in the document. We propose a new “Bag of Phrases” model to capture this discriminative power of phrases for text classification. We present a novel algorithm to extract phrases from the corpus using the well known topic model, Latent Dirichlet Allocation(LDA), and to integrate them in vector space model for classification. Experiments show a better performance of classifiers with the new Bag of Phrases model against related representation models.

Automatic classification of scientific records using the German Subject Heading Authority File (SWD)

The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

An ontology based for drilling report classification

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

Metodi di previsione di indici di borsa con tecniche di text & opinion mining applicate a twitter

Il problema relativo alla predizione, la ricerca di pattern predittivi all‘interno dei dati, è stato studiato ampiamente. Molte metodologie robuste ed efficienti sono state sviluppate, procedimenti che si basano sull‘analisi di informazioni numeriche strutturate. Quella testuale, d‘altro canto, è una tipologia di informazione fortemente destrutturata. Quindi, una immediata conclusione, porterebbe a pensare che per l‘analisi predittiva su dati testuali sia necessario sviluppare metodi completamente diversi da quelli ben noti dalle tecniche di data mining. Un problema di predizione può essere risolto utilizzando invece gli stessi metodi : dati testuali e documenti possono essere trasformati in valori numerici, considerando per esempio l‘assenza o la presenza di termini, rendendo di fatto possibile una utilizzazione efficiente delle tecniche già sviluppate. Il text mining abilita la congiunzione di concetti da campi di applicazione estremamente eterogenei. Con l‘immensa quantità di dati testuali presenti, basti pensare, sul World Wide Web, ed in continua crescita a causa dell‘utilizzo pervasivo di smartphones e computers, i campi di applicazione delle analisi di tipo testuale divengono innumerevoli. L‘avvento e la diffusione dei social networks e della pratica di micro blogging abilita le persone alla condivisione di opinioni e stati d‘animo, creando un corpus testuale di dimensioni incalcolabili aggiornato giornalmente. Le nuove tecniche di Sentiment Analysis, o Opinion Mining, si occupano di analizzare lo stato emotivo o la tipologia di opinione espressa all‘interno di un documento testuale. Esse sono discipline attraverso le quali, per esempio, estrarre indicatori dello stato d‘animo di un individuo, oppure di un insieme di individui, creando una rappresentazione dello stato emotivo sociale. L‘andamento dello stato emotivo sociale può condizionare macroscopicamente l‘evolvere di eventi globali? Studi in campo di Economia e Finanza Comportamentale assicurano un legame fra stato emotivo, capacità nel prendere decisioni ed indicatori economici. Grazie alle tecniche disponibili ed alla mole di dati testuali continuamente aggiornati riguardanti lo stato d‘animo di milioni di individui diviene possibile analizzare tali correlazioni. In questo studio viene costruito un sistema per la previsione delle variazioni di indici di borsa, basandosi su dati testuali estratti dalla piattaforma di microblogging Twitter, sotto forma di tweets pubblici; tale sistema include tecniche di miglioramento della previsione basate sullo studio di similarità dei testi, categorizzandone il contributo effettivo alla previsione.