In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.ĭeep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. Experiments on Twitter data show the efficacy of the model in generating memes for sentences in online social interaction. An experiment is designed to score how well the generated memes can represent the tweets from Twitter conversations. The quality of the generated captions and the generated memes is evaluated through both automated and human evaluation. The model learns the dependencies between the meme captions and the meme template images and generates new memes using the learned dependencies. The generated natural language meme caption is conditioned on the input sentence and the selected meme template. An encoder is used to map the selected meme template and the input sentence into a meme embedding and a decoder is used to decode the meme caption from the meme embedding. For a given input sentence, an image meme is generated by combining a meme template image and a text caption where the meme template image is selected from a set of popular candidates using a selection module, and the meme caption is generated by an encoder-decoder model. This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-based meme for any given sentence using an encoder-decoder architecture. Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers.
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