Adapting Sequence to Sequence models for Text Normalization in Social Media

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.Comment: Accepted at the 13th International AAAI Conference on Web and Social Media (ICWSM 2019

Learning to Use Normalization Techniques for Preprocessing and Classification of Text Documents

Text classification is the most substantial area in natural language processing. In this task, the text document is divided into various types according to the researcher’s purpose. In the text classification process, the basic phase is text preprocessing. In text preprocessing, cleaning, and preparing text data are significant tasks. To accomplish these tasks under the text preprocessing, normalization techniques play a major role. Different kinds of normalization techniques are available. In this research, we mainly focus on different normalization techniques and the way of applying them to text preprocessing. Normalization techniques reduce the words of the text files and change the word form to another form. It helps to analyze the unstructured texts and predefine the text into standard form. This causes to improve the efficiency and performance of the text classification process. For text classification, it is important to extract the most reliable and relevant words of the text files, because feature extraction causes successful classification. This study includes the lowercasing, tokenization, stop word removal, and lemmatization as normalization techniques. 200 text documents from two different domains, namely, formal news articles and informal letters obtained from the Internet in the English language were evaluated using these normalization techniques. The experimental results show the effectiveness of the use of normalization techniques for the preprocessing and classification of text documents and for comparison between before and after using normalization techniques to the text files. Based on the comparison, we identified that these normalization techniques help to clean and prepare text data for effective and accurate text document classification. KEYWORDS: Preprocessing, Normalization, Techniques, Cleaning documents, Text classificati

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not be suitable for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these problems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can be applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts using Naïve Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naïve Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The best sentiment classification performance using proposed feature expansion is obtained by accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large number of them

Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings

Text Classification in an Under-Resourced Language via Lexical Normalization and Feature Pooling

Automatic classification of textual content in an under-resourced language is challenging, since lexical resources and preprocessing tools are not available for such languages. Their bag-of-words (BoW) representation is usually highly sparse and noisy, and text classification built on such a representation yields poor performance. In this paper, we explore the effectiveness of lexical normalization of terms and statistical feature pooling for improving text classification in an under-resourced language. We focus on classifying citizen feedback on government services provided through SMS texts which are written predominantly in Roman Urdu (an informal forward transliterated version of the Urdu language). Our proposed methodology performs normalization of lexical variations of terms using phonetic and string similarity. It subsequently employs a supervised feature extraction technique to obtain category-specific highly discriminating features. Our experiments with classifiers reveal that significant improvement in classification performance is achieved by lexical normalization plus feature pooling over standard representations