Sentiment analysis is a gateway to AI-based text analysis. For any company or data scientist looking to extract meaning out of an unstructured text corpus, sentiment analysis is one of the first steps which gives a high RoI of additional insights with relatively low investment of time and effort. With an explosion of text data available in digital formats, the need for sentiment analysis and other NLU techniques for analyzing this data is growing rapidly. Sentiment analysis looks relatively simple and works very well today, but we have reached hereafter significant efforts by researchers who have invented different approaches and tried numerous models. In the chart above, we give a snapshot to the reader about the different approaches tried and their corresponding accuracy on the IMDB dataset.

If a person wishes to relax himself, travelling is probably the best pick for most people. Choosing the right place to stay for your vocation is one of the most important parts in a travel, but how to do so may be a problem. Reading through reviews of a certain hotel may be a good choice, referring to visitors' experience, you get to know some more specific details about the hotel, however, this method is not comprehensive enough, and reading a bunch of reviews would irritate you. Here is a way I would like to introduce to y'all, easy, fast and accurate, conducting a sentiment analysis. Sentiment analysis, also known as opinion mining, is the process of computationally identify and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc.

NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within sentiment, emotion and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.

Welcome back to Data Science 101! Do you have text data? Do you want to figure out whether the opinions expressed in it are positive or negative? Then you've come to the right place! Today, we're going to get you up to speed on sentiment analysis. If you're the hands-on type, you might want to head directly to the notebook for this tutorial.

Amazon Machine Learning is a part of the Amazon Artifical Intelligence (AI) family, which includes the Amazon Rekognition, Amazon Lex, and Amazon Polly services. Amazon Machine Learning provides AWS customers with an easy way to take advantage of the benefits of complex machine learning capabilities without requiring extensive AI domain expertise. The Machine Learning service enables easy addition of features like fraud detection, sentiment analysis, and customer churn prediction to applications and products. The Amazon Machine Learning service was announced at the 2016 AWS re:Invent conference along with other members of the AI family, Rekognition, Lex, & Polly.

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments; regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.

Sentiment analysis, sometimes called opinion mining, is one of the easiest and quickest ways to find out what consumers are thinking about a brand, product or event. It's a natural language processing technique often used in social listening scenarios, that aims to systematically identify opinions in a document and give it a score of positive, negative or neutral. There are few things as mind-numbingly tedious as manually tagging documents with the right sentiment because the technology doesn't get it. Sentiment analysis (ironically) has a bad reputation in the social listening industry, because truth be told, it needs a lot of manual work to deliver great results. Our data science guys (the brains behind our award winning image recognition technology) have been working on fixing this behind the scenes, and I'm excited to finally share their fantastic results.

Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification accuracy when used in conjunction with sentiment analysis algorithms. An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss in sentiment classification accuracy for (20%, 50%, 75%) data compression with PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other sentiment analysis algorithms are more severely affected by compression. We conclude that significant compression of text data is possible for sentiment analysis depending on the accuracy demands of the specific application and the specific sentiment analysis algorithm used.

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