"When dealing with people, remember you are not dealing with creatures of logic, but creatures of emotion" – Dale Carnegie. Emotion plays a critical role in our daily lives. Be it in shaping our relationships or day-to-day brand choices, we look for a connect at some level. And companies that tap into this emotion and get it right are usually the ones customers flock to. They are also the ones to turn customers into loyal, lifelong evangelists.

Media giant AMC Networks reveals how machine learning and a data-centric approach helps them build intelligent applications faster and leading financial services company, USAA discusses how machine learning is a strategic initiative for their enterprise. Silicon Valley innovation expert Jeremiah Owyang shares experiences working with brand-name companies to navigate business disruption. Hear from a special guest --- Garry Kasparov World-renowned chess champion, visionary, and author Garry Kasparov shares his thoughts on a future where intelligent machines help us turn our grandest dreams into reality.

Since 2010, Facebook allows you to download an archive file of all your interactions with the network. It's a 5-click easy process that your grandmother can do (more details below). Visually, it looks like an ad-free stripped down version of Facebook that's actually quite relaxing. As I'm trying to reduce my exposure to social networks, I decided to take a look at this info. By extrapolating the data of a single individual (me), I might be able to better apprehend the capabilities of the beast.

Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there's a lot of data to analyse and to play with. This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we'll see different options to collect data from Twitter.

The analysis of texts to determine the writers' or speakers' opinion and attitude expressed, and how the results can be used. Sentiment analysis is also known as opinion mining. In its simplest form, it's a way of determining how positive or negative the content of a text document is, based on the relative numbers of words it contains that are classified as either positive or negative. Positive words would include words such as'amazing', 'friendly', 'clean', 'exceeded', and'prompt'. Negative words could be words like'scam', 'unprofessional', 'rude', 'refund', and'incompetent'.

We have constructed an information extraction system called the Mars Target Encyclopedia that takes in planetary science publications and extracts scientific knowledge about target compositions. The extracted knowledge is stored in a searchable database that can greatly accelerate the ability of scientists to compare new discoveries with what is already known. To date, we have applied this system to ~6000 documents and achieved 41-56% precision in the extracted information.

Inspired by recent works in Aspect-Based Sentiment Analysis (ABSA) on product reviews and faced with more complex posts on social media platforms mentioning multiple entities as well as multiple aspects, we define a novel task called Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA). This task aims at fine-grained sentiment analysis of (entity, aspect) combinations, making the well-studied ABSA task a special case of it. To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method. Our experimental results show that our CEA method achieves a significant gain over several baselines, including the state-of-the-art method for the ABSA task, and their enhanced versions, on datasets for ME-ABSA and ABSA tasks. The in-depth analysis illustrates the significant advantage of the CEA method over baseline methods for several hard-to-predict post types. Furthermore, we show that the CEA method is capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

For many languages and domains, there is a paucity of available Given such valuable data, modern deep learning-based sentiment data and resources. In some cases, it may be challenging analysis methods excel at determining the sentiment to obtain sufficient in-domain training data, both because polarity of what is being said about companies, products, etc. there may be less data available online and because it may be (Wang et al. 2015). Unfortunately, such deep methods require somewhat harder to find annotators. Hence, a question that substantial amounts of training data, because multiple levels arises is whether one can assist deep networks by incorporating of computation, each with additional weights and parameters, external cues that enable the model to generalize better.

Deep neural networks have gained great success recently for sentiment classification. However, these approaches do not fully exploit the linguistic knowledge. In this paper, we propose a novel sentiment lexicon enhanced attention-based LSTM (SLEA-LSTM) model to improve the performance of sentence-level sentiment classification. Our method successfully integrates sentiment lexicon into deep neural networks via single-head or multi-head attention mechanisms. We conduct extensive experiments on MR and SST datasets. The experimental results show that our model achieved comparable or better performance than the state-of-the-art methods.

Neural network methods have achieved great success in reviews sentiment classification. Recently, some works achieved improvement by incorporating user and product information to generate a review representation. However, in reviews, we observe that some words or sentences show strong user's preference, and some others tend to indicate product's characteristic. The two kinds of information play different roles in determining the sentiment label of a review. Therefore, it is not reasonable to encode user and product information together into one representation. In this paper, we propose a novel framework to encode user and product information. Firstly, we apply two individual hierarchical neural networks to generate two representations, with user attention or with product attention. Then, we design a combined strategy to make full use of the two representations for training and final prediction. The experimental results show that our model obviously outperforms other state-of-the-art methods on IMDB and Yelp datasets. Through the visualization of attention over words related to user or product, we validate our observation mentioned above.