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

Sentiment analysis in context: Investigating the use of BERT and other techniques for ChatBot improvement

openIn an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent. Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks. The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction. The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself. This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis. Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all. Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task. During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis. Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production. The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment.In an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent. Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks. The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction. The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself. This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis. Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all. Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task. During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis. Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production. The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment

Interactive Attention Networks for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.Comment: Accepted by IJCAI 201

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1