Understanding Language in Conversations "The problems addressed in discourse research aim to answer two general kinds of questions: (1) what information is contained in extended sequences of utterances that goes beyond the meaning of the individual utterances themselves? (2) how does the context in which an utterance is used affect the meaning of the individual utterances, or parts of them?"
– Barbara Grosz. Overview of Chapter 6: Discourse and Dialogue, Survey of the State of the Art in Human Language Technology (1996).
Sentiment essentially relates to feelings; attitudes, emotions and opinions. Sentiment Analysis refers to the practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. A person's opinion or feelings are for the most part subjective and not facts. Which means to accurately analyze an individual's opinion or mood from a piece of text can be extremely difficult. With Sentiment Analysis from a text analytics point of view, we are essentially looking to get an understanding of the attitude of a writer with respect to a topic in a piece of text and its polarity; whether it's positive, negative or neutral.
From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines. The same can be said for the research being done in natural language processing (NLP). To highlight some of the work being done in the field, below are five essential papers on sentiment analysis and sentiment classification.
Should being comparable to BERT really be your goal here? The thing about BERT is that it wasn't really specifically designed for sentiment analysis. It just happens that it does that well too. But there's no reason to believe it's anywhere close to the "best way" to do sentiment analysis. I mean, as an analogy, pulling out a calculator to make a quick computation is often more convenient than booting up Matlab to do it, but using this fact to extol the merits of calculator kind of misses the point.
While it's not uncommon for small and medium-sized businesses (SMBs) to switch financial institutions, the 2019 FIS Performance Against Customer Expectations report has found that the rate of churn is increasing. Historically, 13%-15% of small and medium-sized firms have been found to be actively reviewing their banking relationships. However, the turnover rate has now risen to 61% among the top 50 U.S. banks and 60% among regional banks. All it may take to push an already skeptical firm to switch is one more bad experience. So customer sentiment analysis could be exactly what financial institutions need to improve customer experience -- ideally, before things ever reach that pass.
I recently built a movie recommender that takes as input a user written passage about liked and/or disliked movies. At the onset of the project I figured that determining which movies users' liked and disliked would be simple. After all, using text to determine whether someone likes or dislike a movie doesn't seem too ambitious. With the variety of packages readily available for sentiment analysis in python, there had to be something available out of the box to do this job. As it turns out, using text to determine whether someone likes vs dislikes a movie, or any named entity, is deceivingly complex.
Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.
If so, you've come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services. Sentiment analysis is the process of identifying the emotion and/or opinion within unstructured text. The text can be in the form of customer reviews, social media posts, and more. This process allows you to accurately gauge customer opinion about your brand, products, or services.
Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.
Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.