Update README.md

README.md

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 **Associating specific emotions to short sequences of texts**
 
-We propose using "multi-channel" combinations of convolutional kernels (ala CNN) and Long Short-Term Memory (LSTM) units to classify short text sequences (in our case, tweets) into one of five emotional classes, as opposed to the typical binary (positive/negative) or ternary (positive/negative/neutral) classes.
+We propose using "multi-channel" combinations of convolutional kernels (aka CNN) and Long Short-Term Memory (LSTM) units to classify short text sequences (in our case, tweets) into one of five emotional classes, as opposed to the typical binary (positive/negative) or ternary (positive/negative/neutral) classes.
 
 Classifing short sequences of text into many classes is still a relatively uncommon topic of research. In particular, with the exception of Bouazizi and Ohtsuki (2017), few authors describe the effectiveness of classifing short text sequences (such as tweets) into anything more than 3 distinct classes (positive/negative/neutral). In particular, Bouazizi and Ohtsuki only achieved an overall accuracy of 56.9% or 60.2% on seven distinct classes in two of their papers. We hope to propose a new approach that can improve classification accuracy by a appreciable amount.
 
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 **Contributors**: Tong Hui Kang, Chia Yew Ken
 
-**Institution**: Singapore University of Technology and Design (SUTD)
+**Institution**: Singapore University of Technology and Design (SUTD)