22,434 research outputs found
Latent dirichlet markov allocation for sentiment analysis
In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model
Preparation of Improved Turkish DataSet for Sentiment Analysis in Social Media
A public dataset, with a variety of properties suitable for sentiment
analysis [1], event prediction, trend detection and other text mining
applications, is needed in order to be able to successfully perform analysis
studies. The vast majority of data on social media is text-based and it is not
possible to directly apply machine learning processes into these raw data,
since several different processes are required to prepare the data before the
implementation of the algorithms. For example, different misspellings of same
word enlarge the word vector space unnecessarily, thereby it leads to reduce
the success of the algorithm and increase the computational power requirement.
This paper presents an improved Turkish dataset with an effective spelling
correction algorithm based on Hadoop [2]. The collected data is recorded on the
Hadoop Distributed File System and the text based data is processed by
MapReduce programming model. This method is suitable for the storage and
processing of large sized text based social media data. In this study, movie
reviews have been automatically recorded with Apache ManifoldCF (MCF) [3] and
data clusters have been created. Various methods compared such as Levenshtein
and Fuzzy String Matching have been proposed to create a public dataset from
collected data. Experimental results show that the proposed algorithm, which
can be used as an open source dataset in sentiment analysis studies, have been
performed successfully to the detection and correction of spelling errors.Comment: Presented at CMES201
Automatic stance detection on political discourse in Twitter
The majority of opinion mining tasks in natural language processing (NLP) have been focused on sentiment analysis of texts about products and services while there is comparatively less research on automatic detection of political opinion. Almost all previous research work has been done for English, while this thesis is focused on the automatic detection of stance (whether he or she is favorable or not towards important political topic) from Twitter posts in Catalan, Spanish and English. The main objective of this work is to build and compare automatic stance detection systems using supervised both classic machine and deep learning techniques. We also study the influence of text normalization and perform experiments with differentt methods for word representations such as TF-IDF measures for unigrams, word embeddings, tweet embeddings, and contextual character-based embeddings. We obtain state-of-the-art results in the stance detection task on the IberEval 2018 dataset. Our research shows that text normalization and feature selection is important for the systems with unigram features, and does not affect the performance when working with word vector representations. Classic methods such as unigrams and SVM classifier still outperform deep learning techniques, but seem to be prone to overfitting. The classifiers trained using word vector representations and the neural network models encoded with contextual character-based vectors show greater robustness
Automatic stance detection on political discourse in Twitter
The majority of opinion mining tasks in natural language processing (NLP) have been focused on sentiment analysis of texts about products and services while there is comparatively less research on automatic detection of political opinion. Almost all previous research work has been done for English, while this thesis is focused on the automatic detection of stance (whether he or she is favorable or not towards important political topic) from Twitter posts in Catalan, Spanish and English. The main objective of this work is to build and compare automatic stance detection systems using supervised both classic machine and deep learning techniques. We also study the influence of text normalization and perform experiments with differentt methods for word representations such as TF-IDF measures for unigrams, word embeddings, tweet embeddings, and contextual character-based embeddings. We obtain state-of-the-art results in the stance detection task on the IberEval 2018 dataset. Our research shows that text normalization and feature selection is important for the systems with unigram features, and does not affect the performance when working with word vector representations. Classic methods such as unigrams and SVM classifier still outperform deep learning techniques, but seem to be prone to overfitting. The classifiers trained using word vector representations and the neural network models encoded with contextual character-based vectors show greater robustness
A Multimodal Approach to Sarcasm Detection on Social Media
In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly more difficult than categorizing sentiment. Because, in sarcasm, the intended meaning of the expression by the user is opposite to the literal meaning. Because of its complex nature, it is often difficult even for human to detect sarcasm without proper context. However, people on social media succeed in detecting sarcasm despite interacting with strangers across the world. That motivates us to investigate the human process of detecting sarcasm on social media where abundant context information is often unavailable and the group of users communicating with each other are rarely well-acquainted. We have conducted a qualitative study to examine the patterns of users conveying sarcasm on social media. Whereas most sarcasm detection systems deal in word-by-word basis to accomplish their goal, we focused on the holistic sentiment conveyed by the post. We argue that utilization of word-level information will limit the systems performance to the domain of the dataset used to train the system and might not perform well for non-English language. As an endeavor to make our system less dependent on text data, we proposed a multimodal approach for sarcasm detection. We showed the applicability of images and reaction emoticons as other sources of hints about the sentiment of the post. Our research showed the superior results from a multimodal approach when compared to a unimodal approach. Multimodal sarcasm detection systems, as the one presented in this research, with the inclusion of more modes or sources of data might lead to a better sarcasm detection model
Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM
User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings
Predictive model for detecting fake reviews: Exploring the possible enhancements of using word embeddings
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceFake data contaminates the insights that can be obtained about a product or service and ultimately hurts both businesses and consumers. Being able to correctly identify the truthful reviews will ensure consumers are able to more effectively find products that suit their needs. The following paper aims to develop a predictive model for detecting fake hotel reviews using Natural Language Processing techniques and applying various Machine Learning models. The current research in this area has primarily focused on sentiment analysis and the detection of fake reviews using various text mining methods including bag of words, tokenization, POS tagging and TF-IDF. The research mostly looks at some combination of quantitative and qualitative information. The text component is only analyzed with regards to which words appear in the review, while the semantic relationship is ignored. This research attempts to develop a higher level of performance by implementing pretrained word embeddings during the preprocessing of the text data. The goal is to introduce some context to the text data and see how each model’s performance changes. Traditional text mining models were applied to the dataset to provide a benchmark. Subsequently, GloVe, Word2Vec and BERT word embeddings were implemented and the performance of 8 models was reviewed. The analysis shows a somewhat lower performance obtained by the word embeddings. It seems that in texts of short length, the appearance of words is more indicative of a fake review than the semantic meaning of those words
Opinion Mining on Non-English Short Text
As the type and the number of such venues increase, automated analysis of
sentiment on textual resources has become an essential data mining task. In
this paper, we investigate the problem of mining opinions on the collection of
informal short texts. Both positive and negative sentiment strength of texts
are detected. We focus on a non-English language that has few resources for
text mining. This approach would help enhance the sentiment analysis in
languages where a list of opinionated words does not exist. We propose a new
method projects the text into dense and low dimensional feature vectors
according to the sentiment strength of the words. We detect the mixture of
positive and negative sentiments on a multi-variant scale. Empirical evaluation
of the proposed framework on Turkish tweets shows that our approach gets good
results for opinion mining
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
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