Discourse & Dialogue


Exploratory Data Analysis for Natural Language Processing

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This article was originally posted by Shahul ES on the Neptune blog. Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different. But which tools you should choose to explore and visualize text data efficiently? In this article, we will discuss and implement nearly all the major techniques that you can use to understand your text data and give you a complete(ish) tour into Python tools that get the job done. In this article, we will use a million news headlines dataset from Kaggle. Now, we can take a look at the data. The dataset contains only two columns, the published date, and the news heading.


What AI-based Sentiment Analysis Can Tell Us About Fintech and Neobanks

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Over the past decade, fintech firms have set out to reinvent banking and financial services. One major market trend is the growth of the neobank, a new type of bank that is 100% digital. Instead of using physical branch networks, neobanks service customers using software and applications, allowing customers to transact on their mobile devices and providing accounts with much lower fees and more features. This trend to digitizing banking and the exchange of value is a natural progression of the information revolution to embrace digital. Fintech is an exciting market that continues to grow.


AI Analysis Shows Improvement in Conservation of Endangered Species

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Researchers using artificial intelligence to grade decades of conservation efforts have determined we're getting better at reintroducing once-endangered species to the wild. In their study published Thursday in the journal Patterns, the researchers analyzed the abstracts of more than 4,000 studies of species reintroduction across four decades and found that we're generally improving in our conservation efforts. The authors hope that machine learning could be used in this field, as well as others, to discover the best techniques and solutions from the ever-growing plethora of scientific research. "We wanted to learn some lessons from the vast body of conservation biology literature on reintroduction programs that we could use here in California as we try to put sea otters back into places they haven't roamed for decades," said senior author Kyle Van Houtan, chief scientist at Monterey Bay Aquarium in California. "But what sat in front of us was millions of words and thousands of manuscripts. We wondered how we could extract data from them that we could actually analyze, and so we turned to natural language processing."


Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Neural Information Processing Systems

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r .7,


Discriminative Topic Modeling with Logistic LDA

Neural Information Processing Systems

Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging for practitioners. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant of latent Dirichlet allocation which is easy to apply to arbitrary inputs. In particular, our model can easily be applied to groups of images, arbitrary text embeddings, or integrate deep neural networks. Although it is a discriminative model, we show that logistic LDA can learn from unlabeled data in an unsupervised manner by exploiting the group structure present in the data.


Sentiment Analysis Exposed

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I made it with Max last night! OMG! Welcome to womanhood!! How was it/he? And right about now, Mary's mom gets a'notification' on her cell phone that her daughter is texting sexual references, then displays Mary's texts with Shelly upon mom's request. Mom spends the rest of the day at work fuming, conjuring dialog with her daughter for later that evening when they'll be home together. Never did, and she'd told Mary not to see him.


Classifying IMDB sentiment with Keras and Embeddings, Dropout & Conv1D – MachineCurve

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However, let's add a few evaluation & visualization parts before doing so – for you to visually appreciate model progress. First, we add a numerical evaluation using model.evaluate


Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back

arXiv.org Machine Learning

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as ``bank.'' In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors.


Gated Mechanism for Attention Based Multimodal Sentiment Analysis

arXiv.org Machine Learning

ABSTRACT different granularities [3, 9] or use a cross interaction block that couple the features from different modalities [10, 6]. It is imperative that all modalities in multimodal interactions and 3. Fusion of unimodal and cross Therefore, to learn better cross modal information, we introduce 1.6% and 1.34% absolute improvement over current state-ofthe-art. Furthermore, to capture long term dependencies across 1. INTRODUCTION These are categorised into three types, 1. Methods that learn the modalities independently and fuse the In our proposed model, we aim to learn the interaction between [3, 4], and 3. Methods that explicitly learn contributions Personal use of this material is permitted. Multimodal sentiment analysis provides an opportunity to 2.1. M T V H T W H T V; W R d d (3) (U 1, U 2,..., U u) for a Text modality can be defined as: Cross attentive representations of Text (C V T R u d) and H T Bi-GRU(U 1, U 2,..., U u) (1) Video (C T V R u d) can be represented as: Subscript T denotes Text modality, A and V represent Audio As much as there is an opportunity to leverage cross modal interactions, representations is employed.


Analyzing Customer Support on Social Media - Qualetics Data Machines

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The goal of this study is to analyze the queries raised by customers on a particular social media platform by analyzing their interactions with the customer support and provide incisive insights to perform sentiment analysis. We performed exploratory data analysis to extract insights from the data. With Deep Learning tools like NLTK, sentiment analysis was performed to understand the positive, negative, and neutral sentiments of the customers of a brand. Machine Learning was used to identify the frequency of similar text appearances. Deep learning algorithms were used to understand the customer queries and the average time taken by the respective company's social customer support team in addressing the queries.