The SKLearn lab will have a tutorial for sentiment analysis and mnist (via a Google Colab Notebook) with emphasis on how to improve performance, then time for students to try their own classifiers on a separate sentiment analysis task. The PyTorch lab willhave a tutorial on PyTorch and how to build feed-forward nets for the same tasks as in the Sklearn lab (with emphasis on how to improve performance), and time for students to try to build their own network for the separate sentiment analysis task.

I recently built a movie recommender that takes as input a user written passage about liked and/or disliked movies. At the onset of the project I figured that determining which movies users' liked and disliked would be simple. After all, using text to determine whether someone likes or dislike a movie doesn't seem too ambitious. With the variety of packages readily available for sentiment analysis in python, there had to be something available out of the box to do this job. As it turns out, using text to determine whether someone likes vs dislikes a movie, or any named entity, is deceivingly complex.

Sign in to report inappropriate content. Our CTO & Co- Founder, Jesus Rodriguez explores the benefits and challenges of traditional techniques such as sentiment analysis, topic/entity extraction or tone analysis methods when it comes to analyzing crypto-assets. We also show how detailed social media analysis techniques can show relevant insights about the behavior of the crypto markets.

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.

If so, you've come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services. Sentiment analysis is the process of identifying the emotion and/or opinion within unstructured text. The text can be in the form of customer reviews, social media posts, and more. This process allows you to accurately gauge customer opinion about your brand, products, or services.

Namboodiri Indian Institute of Technology, Kanpur { badri,sandepkr,vinodkk,vinaypn} @iitk.ac.in Abstract Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr). 1 Introduction To understand the progress towards multimedia vision and language understanding, a visual Turing test was proposed by (Geman et al., 2015) that was aimed at visual question answering (Antol et al., 2015). Visual Dialog (Das et al., 2017) is a natural extension for VQA. Current dialog systems as evaluated in (Chattopadhyay et al., 2017) show that when trained between bots, AIAI dialog systems show improvement, but that does not translate to actual improvement for Human-AI dialog. This is because, the questions generated by bots are not natural (humanlike) and therefore does not translate to improved human dialog. Therefore it is imperative that improvement in the quality of questions will enable dialog agents to perform well in human interactions. Further, (Ganju et al., 2017) show that unanswered questions can be used for improving VQA, Image captioning and Object Classification. An interesting line of work in this respect is the work of (Mostafazadeh et al., 2016). Here the authors have proposed the challenging task of generating natural questions for an image. One aspect that is central to a question is the context that is relevant to generate it. As can be seen in Figure 1, an image with a person on a skateboard would result in questions related to the event.

Although there's quite some research done on irony detection, there's still not enough data generated to use for the actual training. The solution can be a large-scale irony dataset, which will allow to create complex training models and provide more accurate sentiment analysis. In order to address the lack of irony data the study analyzes more than 2 million tweets and proposes a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way. This is the first research that generates ironic sentences to achieve a high irony accuracy with well-preserved sentiment and content.

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. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social media and have pressured congress to restrict political advertising on social media. Following the related legislation, social media companies began disclosing their political ads archive for transparency during the summer of 2018 when the midterm election campaign was just beginning. This research collects the data of the related political ads in the context of the U.S. midterm elections since August to study the overall pattern of political ads on social media and uses sets of machine learning methods to conduct sentiment analysis on these ads to classify the negative ads. A novel approach is applied that uses AI image recognition to study the image data. Through data visualization, this research shows that negative advertising is still the minority, Republican advertisers and third party organizations are more likely to engage in negative advertising than their counterparts. Based on ordinal regressions, this study finds that anger evoked information-seeking is one of the main mechanisms causing negative ads to be more engaging and effective rather than the negative bias theory. Overall, this study provides a unique understanding of political advertising on social media by applying innovative data science methods. Further studies can extend the findings, methods, and datasets in this study, and several suggestions are given for future research.

Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora. To make the summarization results more faithful, this paper presents an unsupervised approach that combines rhetorical structure theory, deep neural model and domain knowledge concern for ATS. This architecture mainly contains three components: domain knowledge base construction based on representation learning, attentional encoder-decoder model for rhetorical parsing and subroutine-based model for text summarization. Domain knowledge can be effectively used for unsupervised rhetorical parsing thus rhetorical structure trees for each document can be derived. In the unsupervised rhetorical parsing module, the idea of translation was adopted to alleviate the problem of data scarcity. The subroutine-based summarization model purely depends on the derived rhetorical structure trees and can generate content-balanced results. To evaluate the summary results without golden standard, we proposed an unsupervised evaluation metric, whose hyper-parameters were tuned by supervised learning. Experimental results show that, on a large-scale Chinese dataset, our proposed approach can obtain comparable performances compared with existing methods.