  Financial analyses of stock markets rely heavily on quantitative approaches
in an attempt to predict subsequent or market movements based on historical
prices and other measurable metrics. These quantitative analyses might have
missed out on un-quantifiable aspects like sentiment and speculation that also
impact the market. Analyzing vast amounts of qualitative text data to
understand public opinion on social media platform is one approach to address
this gap. This work carried out topic analysis on 28264 financial tweets [1]
via clustering to discover emerging events in the stock market. Three main
topics were discovered to be discussed frequently within the period. First, the
financial ratio EPS is a measure that has been discussed frequently by
investors. Secondly, short selling of shares were discussed heavily, it was
often mentioned together with Morgan Stanley. Thirdly, oil and energy sectors
were often discussed together with policy. These tweets were semantically
clustered by a method consisting of word2vec algorithm to obtain word
embeddings that map words to vectors. Semantic word clusters were then formed.
Each tweet was then vectorized using the Term Frequency-Inverse Document
Frequency (TF-IDF) values of the words it consisted of and based on which
clusters its words were in. Tweet vectors were then converted to compressed
representations by training a deep-autoencoder. K-means clusters were then
formed. This method reduces dimensionality and produces dense vectors, in
contrast to the usual Vector Space Model. Topic modelling with Latent Dirichlet
Allocation (LDA) and top frequent words were used to analyze clusters and
reveal emerging events.
