In today's tech-savvy world every industry is trying to formulate methods for recommending products by combining several techniques and algorithms to form a pool that would bring forward the most enhanced models for making the predictions. Building on these lines is our paper focused on the application of sentiment analysis for recommendation in the insurance domain. We tried building the following Machine Learning models namely, Logistic Regression, Multinomial Naive Bayes, and the mighty Random Forest for analyzing the polarity of a given feedback line given by a customer. Then we used this polarity along with other attributes like Age, Gender, Locality, Income, and the list of other products already purchased by our existing customers as input for our recommendation model. Then we matched the polarity score along with the user's profiles and generated the list of insurance products to be recommended in descending order. Despite our model's simplicity and the lack of the key data sets, the results seemed very logical and realistic. So, by developing the model with more enhanced methods and with access to better and true data gathered from an insurance industry may be the sector could be very well benefitted from the amalgamation of sentiment analysis with a recommendation.

It has been a year since we released (in GA) our last TA API (v3.0). After five previews of adding features, responsible AI, incorporating customer feedback, UX feedback, and optimizations; in July 2021 we announced GA (General Availability) of Text Analytics v3.1. With this release, starting July 2021 customers can use Text Analytics for health, Opinion Mining, PII and Analyze as GA offerings. Text Analytics for health is a feature of the Text Analytics API service that extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. Millions of Text Records were processed during the preview in the last year.

Chinese sentiment analysis (CSA) has always been one of the challenges in natural language processing due to its complexity and uncertainty. Transformer has succeeded in capturing semantic features, but it uses position encoding to capture sequence features, which has great shortcomings compared with the recurrent model. In this paper, we propose T-E-GRU for Chinese sentiment analysis, which combine transformer encoder and GRU. We conducted experiments on three Chinese comment datasets. In view of the confusion of punctuation marks in Chinese comment texts, we selectively retain some punctuation marks with sentence segmentation ability. The experimental results show that T-E-GRU outperforms classic recurrent model and recurrent model with attention.

Sorry to do this, but...close your eyes and think about being stuck on hold: the repetitive music, the periodic sales pitches, the reminders to visit the company's website. But that's not all: VC money is eyeing AI for sales reps, too. "Sales intelligence" tools use AI to listen to salespeoples' conversations with customers, then compile insights that can help drive future revenue. Two of the hottest companies in this space are Chorus.ai Some conversations are just too complex for it to boil down into a set of ones and zeros.

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.

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.

The use of electronic health records in medical research is difficult because of the unstructured format. Extracting information within reports and summarising patient presentations in a way amenable to downstream analysis would be enormously beneficial for operational and clinical research. In this work we present a natural language processing pipeline for information extraction of radiological reports in neurology. Our pipeline uses a hybrid sequence of rule-based and artificial intelligence models to accurately extract and summarise neurological reports. We train and evaluate a custom language model on a corpus of 150000 radiological reports from National Hospital for Neurology and Neurosurgery, London MRI imaging. We also present results for standard NLP tasks on domain-specific neuroradiology datasets. We show our pipeline, called `neuroNLP', can reliably extract clinically relevant information from these reports, enabling downstream modelling of reports and associated imaging on a heretofore unprecedented scale.

Working with an enormous amount of text data is always hectic and time-consuming. Hence, many companies and organisations rely on Information Extraction techniques to automate manual work with intelligent algorithms. Information extraction can reduce human effort, reduce expenses, and make the process less error-prone and more efficient. It will also cover use-cases, challenges and discuss how to set up information extraction NLP workflows for your business. For example, consider we're going through a company's financial information from a few documents.

Automatic sentiment analysis play vital role in decision making. Many organizations spend a lot of budget to understand their customer satisfaction by manually going over their feedback/comments or tweets. Automatic sentiment analysis can give overall picture of the comments received against any event, product, or activity. Usually, the comments/tweets are classified into two main classes that are negative or positive. However, the negative comments are too abstract to understand the basic reason or the context. organizations are interested to identify the exact reason for the negativity. In this research study, we hierarchically goes down into negative comments, and link them with more classes. Tweets are extracted from social media sites such as Twitter and Facebook. If the sentiment analysis classifies any tweet into negative class, then we further try to associates that negative comments with more possible negative classes. Based on expert opinions, the negative comments/tweets are further classified into 8 classes. Different machine learning algorithms are evaluated and their accuracy are reported.

GESIS offers an exciting work environment for interdisciplinary research at the interface between social sciences and computer sciences. As an infrastructure institution, we serve the promotion of the social science research, and we are in close cooperation with well-known international research institutes. GESIS supports your PhD, i.e., with our GESIS Doctoral Program. GESIS supports your career development. We offer a wide range of career opportunities in an attractive and rewarding work environment that allows you to carry out your tasks in a creative and autonomous manner.