Sentiment analysis is the process of determining the opinion or feeling of a piece of text and determine whether the writer's attitude towards particular topic or product is positive, negative or neutral. The success of companies or products directly depends on customers. Once we know how the customer feels after analyzing their comments or reviews, they can identify what customer liked and disliked and built things like recommendation systems or more targeted marketing campaigns for them. IMDB dataset used for the project is a large movie review dataset. It consists of 50,000 movie reviews for natural language processing and text analytics.

Aspect-based sentiment classification aims to predict the sentiment polarity of a specific aspect in a sentence. However, most existing methods attempt to construct dependency relations into a homogeneous dependency graph with the sparsity and ambiguity, which cannot cover the comprehensive contextualized features of short texts or consider any additional node types or semantic relation information. To solve those issues, we present a sentiment analysis model named Isomer, which performs a dual-channel attention on heterogeneous dependency graphs incorporating external knowledge, to effectively integrate other additional information. Specifically, a transfer-enhanced dual-channel heterogeneous dependency attention network is devised in Isomer to model short texts using heterogeneous dependency graphs. These heterogeneous dependency graphs not only consider different types of information but also incorporate external knowledge. Experiments studies show that our model outperforms recent models on benchmark datasets. Furthermore, the results suggest that our method captures the importance of various information features to focus on informative contextual words.

Topic evolution modeling has received significant attentions in recent decades. Although various topic evolution models have been proposed, most studies focus on the single document corpus. However in practice, we can easily access data from multiple sources and also observe relationships between them. Then it is of great interest to recognize the relationship between multiple text corpora and further utilize this relationship to improve topic modeling. In this work, we focus on a special type of relationship between two text corpora, which we define as the "lead-lag relationship". This relationship characterizes the phenomenon that one text corpus would influence the topics to be discussed in the other text corpus in the future. To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus. With the recognized lead-lag relationship, the similarities of the two text corpora can be figured out and the quality of topic learning in both corpora can be improved. We numerically investigate the performance of the jointly dynamic topic modeling approach using synthetic data. Finally, we apply the proposed model on two text corpora consisting of statistical papers and the graduation theses. Results show the proposed model can well recognize the lead-lag relationship between the two corpora, and the specific and shared topic patterns in the two corpora are also discovered.

Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words of categories, known as seed words. The recent generative dataless methods construct document-specific category priors by using seed word occurrences only, however, such category priors often contain very limited and even noisy supervised signals. To remedy this problem, in this paper we propose a novel formulation of category prior. First, for each document, we consider its label membership degree by not only counting seed word occurrences, but also using a novel prototype scheme, which captures pseudo-nearest neighboring categories. Second, for each label, we consider its frequency prior knowledge of the corpus, which is also a discriminative knowledge for classification. By incorporating the proposed category prior into the previous generative dataless method, we suggest a novel generative dataless method, namely Weakly Supervised Prototype Topic Model (WSPTM). The experimental results on real-world datasets demonstrate that WSPTM outperforms the existing baseline methods.

In this webinar, the experts from Syskoplan Reply present the Machine Learning abilities of SAP Sales and Service Cloud. Get an overview of the newly added features and learn how to add value to your daily sales and service routines. Get familiar with Machine Learning functions such as Similar Ticket Recommendation, Email Template Recommendation and Machine Translation. The experts demonstrate how Natural Language Processing identifies product numbers, order numbers and language used from a customer's written text. Learn in a live demo how Ticket Categorization and Sentiment Analysis can save valuable time in ticket processing and see how additional valuable information is seamlessly integrated into SAP Service Cloud.

A satisfied customer is the life air of all successful businesses. Every business has one common goal in mind: how to continuously improve customer satisfaction, retain existing customers, and attract more customers. Therefore, customer feedback plays a pivotal role in helping a company understand its customer sentiment about a product and services. With the help of Natural Language Processing (NLP), a company can quickly perform sentiment analysis to gain insightful information about their customer behaviors, patterns and make recommendations. Sentiment Analysis is an extensive and influential topic in natural language processing (NLP) and Machine Learning(ML). Sentiment Analysis is the process of examining a piece of text for opinion and feeling.

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD

Background: When neural network emotion and sentiment classifiers are used in public health informatics studies, biases present in the classifiers could produce inadvertently misleading results. Objective: This study assesses the impact of bias on COVID-19 topics, and demonstrates an automatic algorithm for reducing bias when applied to COVID-19 social media texts. This could help public health informatics studies produce more timely results during crises, with a reduced risk of misleading results. Methods: Emotion and sentiment classifiers were applied to COVID-19 data before and after debiasing the classifiers using unsupervised contrastive clustering. Contrastive clustering approximates the degree to which tokens exhibit a causal versus correlational relationship with emotion or sentiment, by contrasting the tokens' relative salience to topics versus emotions or sentiments. Results: Contrastive clustering distinguishes correlation from causation for tokens with an F1 score of 0.753. Masking bias prone tokens from the classifier input decreases the classifier's overall F1 score by 0.02 (anger) and 0.033 (negative sentiment), but improves the F1 score for sentences annotated as bias prone by 0.155 (anger) and 0.103 (negative sentiment). Averaging across topics, debiasing reduces anger estimates by 14.4% and negative sentiment estimates by 8.0%. Conclusions: Contrastive clustering reduces algorithmic bias in emotion and sentiment classification for social media text pertaining to the COVID-19 pandemic. Public health informatics studies should account for bias, due to its prevalence across a range of topics. Further research is needed to improve bias reduction techniques and to explore the adverse impact of bias on public health informatics analyses.

In this paper, we conduct a sentence level sentiment analysis on the product reviews from Amazon and thorough analysis on the model interpretability. For the sentiment analysis task, we use the BiLSTM model with attention mechanism. For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms. The model has an accuracy of up to 0.96. And we find that the aspect terms have the same or even more attention weights than the sentimental words in sentences.

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.