Yes, but it also groups different words that have the same base form. So biographies and texts about animals might be wrongly grouped together, introducing noise in the corpus. I suspect, depending on the language, that this can happen a lot (or not) and greatly influence the process. I know it helps in Finnish and French and doesn't help in Swedish (with the texts I've used; I have compared LDA output on lemmatised and non-lemmatised versions of the same corpus), I was wondering if you had experience with other languages?

Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, however as the expressiveness of these models grows, so does the difficulty of performing fast and accurate inference over their parameters. This paper presents alternative neural approaches to topic modelling by providing parameterisable distributions over topics which permit training by backpropagation in the framework of neural variational inference. In addition, with the help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to Bayesian non-parametric topic models. Experimental results on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the effectiveness and efficiency of these neural topic models.

The industrial revolution replaced workers with machines, forcing more of them towards the services sector. The digital revolution is now attacking this area through chatbots that aim to be at least as good as entry-level customer service representatives or shopping assistants. Gartner says that by 2020, the customer will manage 85% of their interactions with a company without dealing with a human. The goal is to create conversational instances which don't sound or behave like robots, but as human-like as possible. To achieve this ambitious target, the chatbot needs to understand language, context, tone and even subtle nuances like sarcasm. The tool which can enhance this is sentiment analysis, a process which automatically extracts both the topic and the feeling from the sentence or voice input.

Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set.

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states and achieves state-of-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.

The future of work will depend highly on soft skills. No matter how AI for recruitment and talent assessment is leveraged in the future, a candidate's high-order thinking and EQ will stay vital, something which the robots simply can't replace or automate! This accurate AI-powered tool (beyond IBM Watson) gives you full picture of a candidate's soft skill background (based on the Big 5 personality test, DISC OCEAN, mood graphs, sentiment analysis, digital footprint analysis, behavior score, and much more) to help recruiters spot and process the right'candidates' who would add to their diverse, inclusive company culture. Get a free assessment report, at: https://frrole.ai/deepsense-app/ You just need the twitter handle/ email ID of the individual to get started.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.

User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.

We seek to determine the effectiveness of using location-based social media to predict the outcome of the 2016 presidential election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. Twenty-one states are chosen, with eleven categorized as swing states, five as Clinton favored and five as Trump favored. We incorporate two metrics in polling voter opinion for election outcomes: tweet volume and positive sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to the election outcome per state and across the nation. We use two approaches for determining state victories: winner-take-all and shared elector count. Our results show tweet sentiment mirrors the close races in the swing states; however, the differences in distribution of positive sentiment and volume between Clinton and Trump are not significant using our approach. Thus, we conclude neither sentiment nor volume is an accurate predictor of election results using our collection of data and labeling process.

We propose representing high-dimensional data in 2-dimensions using cliques mapped onto several planes. Currently, Multidimensional Scaling (MDS) projects every point onto an R space medium. However, this may not produce the most ideal result as some relations between points may exhibit higher stress than others. We propose utilizing cliques to extract a complete subset of points into separate facets in order to convey the most accurate distance representation as possible, therefore achieveing low stress in each instance.