Abstract: With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71. 1 INTRODUCTION Sentiment analysis is the process of computationally categorizing and identifying opinions present in a textual document or images. As a field, sentiment analysis has been gaining a lot of interest from the scientific community in recent years. The main motivation for this work comes from the author's observation that there is an unavailability of a system capable of automatically analyzing the sentiment present in citations of scientific publications.

Amazon's newest set of electronic devices was a trending topic in recent times. This use case leverages Data Mining, Natural Language Processing, Machine Learning, and Data Visualization, to build algorithms that perform sentiment analysis on online product reviews and help us understand the consumer sentiments on electronic products available on Amazon. The model can be helpful for any eCommerce business to ascertain the consumer sentiment towards its products and brands. Often, online reviews are large in numbers and are unstructured. Understanding the true intent of the consumers from their reviews can be a difficult task as there arise many barriers such as language ambiguity, sarcasm, irony, and the emojis (emotion icons).

Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.

ThinkPalm is proud to announce that we have been Great Place to Work-Certified by the Great Place to Work Institute, India. The certification testifies the effective practices and policies established and maintained within the organization to provide a productive environment for its people.

Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.

Ever since British economist John Maynard Keynes first declared that investors are prey to people's urge to act, however irrationally, the financial world has tried to quantify the impact of public sentiment on stock prices. Solving the puzzle would give investors in the know a huge advantage over the competition. Over the past decade, one vibrant corner of that still ongoing research has been data analysis. The goal has been to tease out clues about sentiment that are hidden in news articles, regulatory filings, transcripts, and press releases. With the rise of artificial intelligence, the sophistication of sentiment-measuring technology is increasing.

In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different aspects of the entity in question. However, training supervised models for segment-level classification requires segment labels, which may be more difficult or expensive to obtain than review labels. In this paper, we employ Multiple Instance Learning (MIL) and use only weak supervision in the form of a single label per review. First, we show that when inappropriate MIL aggregation functions are used, then MIL-based networks are outperformed by simpler baselines. Second, we propose a new aggregation function based on the sigmoid attention mechanism and show that our proposed model outperforms the state-of-the-art models for segment-level sentiment classification (by up to 9.8% in F1). Finally, we highlight the importance of fine-grained predictions in an important public-health application: finding actionable reports of foodborne illness. We show that our model achieves 48.6% higher recall compared to previous models, thus increasing the chance of identifying previously unknown foodborne outbreaks.

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.

One of the problems our Innovations team is working on at Socialbakers is sentiment analysis. Sentiment analysis is an automated process of recognition of how an audience feels towards any given subject from written or spoken language. It is one of the most common classification tools exploiting artificial intelligence. Sentiment analysis algorithm analyzes given text and predicts whether the underlying sentiment is positive, neutral or negative. Socialbakers is a global AI-powered social media marketing company and there are many use cases for sentiment analysis in our marketing software-as-a-service platform, called the Socialbakers Suite.

Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.