Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres

A Hybrid Environment for Syntax-Semantic Tagging

The thesis describes the application of the relaxation labelling algorithm to NLP disambiguation. Language is modelled through context constraint inspired on Constraint Grammars. The constraints enable the use of a real value statind "compatibility". The technique is applied to POS tagging, Shallow Parsing and Word Sense Disambigation. Experiments and results are reported. The proposed approach enables the use of multi-feature constraint models, the simultaneous resolution of several NL disambiguation tasks, and the collaboration of linguistic and statistical models.Comment: PhD Thesis. 120 page

Using Deep Learning Networks to Predict Telecom Company Customer Satisfaction Based on Arabic Tweets

Information systems are transforming businesses, which are using modern technologies towards new business models based on digital solutions, which ultimately lead to the design of novel socio-economic systems. Sentiment analysis is, in this context, a thriving research area. This paper is a case study of Saudi telecommunications (telecom) companies, using sentiment analysis for customer satisfaction based on a corpus of Arabic tweets. This paper compares, for the first time for Saudi social media in telecommunication, the most popular machine learning approach, support vector machine (SVM), with two deep learning approaches: long short-term memory (LSTM) and gated recurrent unit (GRU). This study used LSTM and GRU with two different implementations, adding attention mechanism and character encoding. The study concluded that the bidirectional-GRU with attention mechanism achieved a better performance in the telecommunication domain and allowed detection of customer satisfaction in the telecommunication domain with high accuracy

Two learning approaches for protein name extraction

Cataloged from PDF version of article.Protein name extraction, one of the basic tasks in automatic extraction of information from biological texts, remains challenging. In this paper, we explore the use of two different machine learning techniques and present the results of the conducted experiments. in the first method, Bigram language model is used to extract protein names. In the latter, we use an automatic rule learning method that can identify protein names located in the biological texts. In both cases, we generalize protein names by using hierarchically categorized syntactic token types. We conducted our experiments on two different datasets. our first method based on Bigram language model achieved an F-score of 67.7% on the YAPEX dataset and 66.8% on the GENIA corpus. The developed rule learning method obtained 61.8% F-score value on the YAPEX dataset and 61.0% on the GENIA corpus. The results of the comparative experiments demonstrate that both techniques are applicable to the task of automatic protein name extraction, a prerequisite for the large-scale processing of biomedical literature. (C) 2009 Elsevier Inc. All rights reserved

Sentiment Analysis Using Machine Learning Techniques

Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset..

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

The Application of Text Mining and Data Visualization Techniques to Textual Corpus Exploration

Unstructured data in the digital universe is growing rapidly and shows no evidence of slowing anytime soon. With the acceleration of growth in digital data being generated and stored on the World Wide Web, the prospect of information overload is much more prevalent now than it has been in the past. As a preemptive analytic measure, organizations across many industries have begun implementing text mining techniques to analyze such large sources of unstructured data. Utilizing various text mining techniques such as n -gram analysis, document and term frequency analysis, correlation analysis, and topic modeling methodologies, this research seeks to develop a tool to allow analysts to maneuver effectively and efficiently through large corpuses of potentially unknown textual data. Additionally, this research explores two notional data exploration scenarios through a large corpus of text data, each exhibiting unique navigation methods analysts may elect to take. Research concludes with the validation of inferential results obtained through each corpus’s exploration scenario