Discourse & Dialogue

The AFINN lexicon is perhaps one of the simplest and most popular lexicons that can be used extensively for sentiment analysis. The current version of the lexicon is AFINN-en-165. You can find this lexicon at the author's official GitHub repository. The author has also created a nice wrapper library on top of this in Python called afinn, which we will be using for our analysis. Let's look at some visualisations now.

Latent Dirichlet Allocation (LDA) is a classical way to do a topic modelling. Topic modeling is a unsupervised learning and the goal is group different document to same "topic". Typical example is clustering a news to corresponding category including "Finance", "Travel", "Sport" etc. Before word embeddings we may use Bag-of-Words in most of the time. However, the world changed after Mikolov et al. introduce word2vec (one of the example of Word Embeddings) in 2013.

We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.

Sentiment analysis is perhaps one of the most popular applications of NLP, with a vast number of tutorials, courses, and applications that focus on analyzing sentiments of diverse datasets ranging from corporate surveys to movie reviews. The key aspect of sentiment analysis is to analyze a body of text for understanding the opinion expressed by it. Typically, we quantify this sentiment with a positive or negative value, called polarity. The overall sentiment is often inferred as positive, neutral or negative from the sign of the polarity score. Usually, sentiment analysis works best on text that has a subjective context than on text with only an objective context.

In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. jLDADMM is open-source and available to download at: https://github.com/datquocnguyen/jLDADMM

BANGKOK – Japanese companies operating in Thailand are upbeat about the business climate thanks to recovery in the automobile sector and its spillover effects to other sectors, with a diffusion index for the second half of this year reaching the highest level in six years. The index for the direction of the business climate in the July-December period rose to 40, up four points from the first half of this year, for the sixth straight half-year improvement, according to a survey by the Japanese Chamber of Commerce, Bangkok. It was the highest positive figure since the latter half of 2012 when the index stood at 41. According to the survey, the indexes for manufacturers and nonmanufacturers in the latter half of this year came to 36 and 45, up from 32 and 41 from the first half, respectively. Business sentiment in sectors such as textiles, general machinery, transportation machinery, trading and retailers improved, while that in sectors like chemicals and construction fell.

Colombo Big Data Meetup August 2nd 2018 Let's analyze the world's reaction to road traffic 2. In a nutshell Social Developer Skills & Interests Recognitions 7 years experience Full stack developer Angular, Big Data enthusiast Automation fanboy Microsoft MVP Developer Technologies Top contributor in the world on Stackoverflow for #Angular, #Cosmosdb Web application architecture Business intelligence Big Data Visualization Azure platform 120 repositories on Stackblitz 4800 answers on Stackoverflow Github contributions D3 directives and more Open-source contributions Sajeetharan Sinnathurai Senior Tech Lead at 99X Technology A few things about me! 3. What is sentiment analysis? "computationally identify and categorize the opinions expressed in a piece of text; determine whether positive/neutral/negative toward a topic/product…" [Oxford Dict.] 4. Why it is so important? What is Logic Apps? • Visual designer without writing single line of code • Dozens of pre-built templates to get started • Out of box support for popular SaaS and on-premises apps • Use with custom API apps of your own • Biztalk APIs for expert integration scenarios 9. Cognitive services Vision Speech Knowledge Language Search "Give your apps a human side" 10. •Sentiment analysis •Key phrase extraction •Topic detection •Language detection 13. Are we? Give away What were the two main Azure resources presented in this session? What is the name of the NOSQL database that could replace MSSQL in the proposed solution?

Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.

There's lots of great projects in Reddit in sentiment analysis, but almost all of the work I've seen focuses on individual posts, as if tweets or reddit comments was simply a list of thumbs up and thumbs down about issues. For example, context, which doesn't seem to get much discussion. One very basic example where this is important: a Reddit comment that itself is booing a negative comment is considered negative. Of course, the nested "negative" comment should actually be counted in favor of the original topic. The relevant fields in NLP would be coreference, and possibly other subfields involving semantics.

In order to feed this data into our RNN, all input documents must have the same length. We start building our model architecture in the code cell below. We have imported some layers from Keras that you might need but feel free to use any other layers / transformations you like. To summarize, our model is a simple RNN model with 1 embedding, 1 LSTM and 1 dense layers. We first need to compile our model by specifying the loss function and optimizer we want to use while training, as well as any evaluation metrics we'd like to measure.