Sentiment is a huge driving factor in the cryptocurrency market. But it is a metric which is very hard to measure. Sentiment analysis has been on the rise for the past few years. With the introduction of new packages, sentiment analysis can be done more quickly and efficiently than ever. In this post, you'll see why looking at the mood on the social media is not a great idea for sentiment analysis.

What does Minority Report, Black Mirror, and 1984 all have in common? Well, turn up to the talk to find out. On a day to day basis we countlessly write notes, send messages and respond to emails. The question is: what does what we write actually show about us, and how can we use the meaning behind these pieces of text to predict crimes and attacks. This talk delves into just this - how machine learning, and specifically natural language processing and sentiment analysis, can be used to predict crime and security attacks.

In this particular case, in only 10 seconds and with the small dataset provided, the CLI tool was able to run quite a few iterations, meaning training multiple times based on different combinations of algorithms/configuration with different internal data transformations and algorithm's hyper-parameters. Finally, the "best quality" model found in 10 seconds is a model using a particular trainer/algorithm with any specific configuration. Depending on the exploration time, the command can produce a different result. The selection is based on the multiple metrics shown, such as Accuracy. The first and easiest metric to evaluate a binary-classification model is the accuracy, which is simple to understand. "Accuracy is the proportion of correct predictions with a test data set.".

The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational Expectation-Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.

This edition of the conference on'Financial Evolution AI, Machine Learning & Sentiment Analysis' by UNICOM Seminars interrogates and explores the implications of AI & ML in the financial services industry. Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results.

This job [Data Scientist] is eating me alive. I spent all these years trying to be the good guy, the man in the white hat. I'm not becoming like them… I am them -- Jake Peralta, Pilot I recently binge-watched a show on Netflix called Brooklyn Nine-Nine and I really enjoyed it. As I eagerly await the release of the next season, I thought it'd be fun to perform exploratory data analysis and sentiment analysis on the pilot episode. I found the script online and extracted the text into CSV file format.

Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and sentiment classification, which classifies tweets into positive, negative and neutral. Since tweets are different from conventional text in that they generally are of limited length and contain informal, irregular or new words, so it is difficult to determine user intention to publish a tweet and user attitude towards certain topic. In this paper, we aim to simultaneously classify tweet purpose, i.e., the intention for user to publish a tweet, and position, i.e., supporting, opposing or being neutral to a given topic. By transforming this problem to a multi-label classification problem, a multi-label classification method with post-processing is proposed. Experiments on real-world data sets demonstrate the effectiveness of this method and the results outperform the individual classification methods.

Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action learning approach is prone to diversities of language surfaces, which may impinge task completion and language quality. To address this issue, we propose multistage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. We model the utterance effect using the transition of dialogue states caused by the utterance and develop a semantic similarity measurement that estimates whether utterances have similar effects. For learning semantic actions on domains without dialogue states, MALA extends the semantic similarity measurement across domains progressively, i.e., from aligning shared actions to learning domain-specific actions. Experiments using multi-domain datasets, SMD and MultiWOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality. 1 Introduction Task-oriented dialogue systems complete tasks for users, such as making a restaurant reservation or scheduling a meeting, in a multi-turn conversation (Gao, Galley, and Li 2018; Sun et al. 2016; Sun et al. 2017).

Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for opinion mining and sentiment analysis. The detection and analysis of sentiment in social media is thus a valuable topic and attracts a lot of research efforts. Most of the earlier efforts focus on supervised learning approaches to solve this problem, which require expensive human annotations and therefore limits their practical use. In our work, we propose a semi-supervised approach to predict user-level sentiments for specific topics. We define and utilize a heterogeneous graph built from the social networks of the users with the knowledge that connected users in social networks typically share similar sentiments. Compared with the previous works, we have several novelties: (1) we incorporate the influences/authoritativeness of the users into the model, 2) we include comment-based and like-based user-user links to the graph, 3) we superimpose multiple heterogeneous graphs into one thereby allowing multiple types of links to exist between two users.