Sentiment analysis involves classifying text into positive, negative and neutral classes according to the emotions expressed in the text. Extensive study has been carried out in performing sentiment analysis using the traditional 'bag of words' approach which involves feature selection, where the input is given to classifiers such as Naive Bayes and SVMs. A relatively new approach to sentiment analysis involves using a deep learning model. In this approach, a recently discovered technique called word embedding is used, following which the input is fed into a deep neural network architecture. As sentiment analysis using deep learning is a relatively unexplored domain, we plan to perform in-depth analysis into this field and implement a state of the art model which will achieve optimal accuracy. The proposed methodology will use a hybrid architecture, which consists of CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), to implement the deep learning model on the SAR14 and Stanford Sentiment Treebank data sets.

In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificially intelligent agents would make them better social learners by allowing them to evaluate human action under a teleological lens. To validate this ability of deep learning models to perform this task, we curate the W-Oops dataset, built upon the Oops dataset [15]. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations. Due to the expensive segment annotation procedure, we propose a weakly supervised algorithm for localizing the goaldirected as well as unintentional temporal regions in the video leveraging solely video-level labels. In particular, we employ an attention mechanism based strategy that predicts the temporal regions which contributes the most to a classification task. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method. We further conduct a video captioning experiment which demonstrates that the proposed localization module does indeed assist teleological action understanding. Project website can be found at: https://asuapg.github.io/TragedyPlusTime.

No abstract