Sarcasm Recognition on News Headlines Using Multiple Channel Embedding Attention BLSTM

Translation of News Headlines

Machine-Translation of news headlines is difficult since the sentences are fragmentary and abbreviations and acronyms of proper names are frequently used. Another difficulty is that, since the headline comes at the top of a news article, the context information useful to disambiguate the sense of words and to determine their translation(target word) is not available. This paper proposes a new a...

Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation

This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and sentiment score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization mo...

Sentiment Analysis of News Headlines using Naïve Bayes Classifier

The amount of user generated content is increasing day by day and it involves detection of opinions about particular topic or an object. Sentiment analysis is used to extract sentiments of people about products, moviews, political events etc. It identifies the viewpoint of opinion holder and polarity of the content i.e. positive negative or neutral. Given the large amount of news being generate...

Analyzing Polls and News Headlines Using Business Intelligence Techniques

Opinion and market research companies gather a substantial amount of polls data which can be combined with news headlines, for the corresponding time periods they are collected. These data are analyzed in order to answer specific (predefined) questions related to the situation of each time period. However, when these tasks are fulfilled, the collected data are archived and possibly the majority...

Negative News No More: Classifying News Article Headlines