Collaborating Authors

Discourse & Dialogue

Performing sentiment analysis on Amazon comments


Currently I am running an experiment to find possible purchase biases in my amazon shopping history, the first step is to run a sentiment analysis in the product comments. For this I am using the Fast Text library for text classification, the creation of this model was based on this article on Kaggle.

What is Sentiment Analysis and how does it impacts Machine Learning


Sentiment analysis (or opinion mining) may be a natural processing technique want to determine whether data is positive, negative, or neutral. Sentiment analysis is usually performed on textual data to assist businesses to monitor brand and merchandise sentiment in customer feedback and understand customer needs. Sentiment analysis is that the process of detecting positive or negative sentiment in text. It's often employed by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an important tool to watch and understand that sentiment.

SugarCRM adds 'sentiment analysis' to SugarPredict AI


SugarCRM unveiled a new tool that the company believes can add "sentiment analysis" to the expanding suite of capabilities within SugarPredict AI. The tool will use natural language processing and AI to provide sales and service personnel with information about a customer's "emotional state and intent." The company said the SugarLive tool will help sales workers "track the details of each customer interaction as it's happening" and "access customer information across all touch points and channels at the exact moment it's needed." The goal of the tool is to bring "next-level, empathic engagement" to sales and service teams, according to a statement from the company. AI techniques are becoming part of every day computing: here's how they're being used to help online retailers keep up with the competition.

Using Sentiment Analysis to Attain and Retain Customers


Matt Canada has a background in graphic design, customer service and management. Sentiment analysis will indicate ways to build a better marketing campaign for your brand. In this article, we will look into four ways to leverage sentiment analysis tools to enhance your brand presence and excite customers. Sentiment analysis is a method to analyze emotions and reactions expressed through online communication - verbal or written. Also termed as'opinion mining' or'emotion AI', sentiment analysis executes data mining, fetches results, and skims out public opinion from within content pieces to help brands get informed of their customer experience.

Over a decade of social opinion mining: a systematic review


Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018.

Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification Artificial Intelligence

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with uncertainty regularization in an iterative manner, IPRLS can adapta single BERT model to work with continuously arriving data from multiple domains while avoiding catastrophic forgetting and interference. Specifically, we leverage an iterative pruning method to remove redundant parameters in large deep networks so that the freed-up space can then be employed to learn new tasks, tackling the catastrophic forgetting problem. Instead of keeping the old-tasks fixed when learning new tasks, we also use an uncertainty regularization based on the Bayesian online learning framework to constrain the update of old tasks weights in BERT, which enables positive backward transfer, i.e. learning new tasks improves performance on past tasks while protecting old knowledge from being lost. In addition, we propose a task-specific low-dimensional residual function in parallel to each layer of BERT, which makes IPRLS less prone to losing the knowledge saved in the base BERT network when learning a new task. Extensive experiments on 16 popular review corpora demonstrate that the proposed IPRLS method sig-nificantly outperforms the strong baselines for lifelong sentiment classification. For reproducibility, we submit the code and data at:

Sentiment Analysis


Sentiment Analysis, as the name suggests, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication. In this article, we will focus on the sentiment analysis of text data. We, humans, communicate with each other in a variety of languages, and any language is just a mediator or a way in which we try to express ourselves. And, whatever we say has a sentiment associated with it.

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset Artificial Intelligence

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.

Zero-Shot Controlled Generation with Encoder-Decoder Transformers Artificial Intelligence

Controlling neural network-based models for natural language generation (NLG) has broad applications in numerous areas such as machine translation, document summarization, and dialog systems. Approaches that enable such control in a zero-shot manner would be of great importance as, among other reasons, they remove the need for additional annotated data and training. In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero-shot. This is done by introducing three control knobs, namely, attention biasing, decoder mixing, and context augmentation, that are applied to these models at generation time. These knobs control the generation process by directly manipulating trained NLG models (e.g., biasing cross-attention layers) to realize the desired attributes in the generated outputs. We show that not only are these NLG models robust to such manipulations, but also their behavior could be controlled without an impact on their generation performance. These results, to the best of our knowledge, are the first of their kind. Through these control knobs, we also investigate the role of transformer decoder's self-attention module and show strong evidence that its primary role is maintaining fluency of sentences generated by these models. Based on this hypothesis, we show that alternative architectures for transformer decoders could be viable options. We also study how this hypothesis could lead to more efficient ways for training encoder-decoder transformer models.

Schema-Guided Paradigm for Zero-Shot Dialog Artificial Intelligence

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.