  In the field of Natural Language Processing, information extraction from
texts has been the objective of many researchers for years. Many different
techniques have been applied in order to reveal the opinion that a tweet might
have, thus understanding the sentiment of the small writing up to 280
characters. Other than figuring out the sentiment of a tweet, a study can also
focus on finding the correlation of the tweets with a certain area of interest,
which constitutes the purpose of this study. In order to reveal if an area of
interest has a trend in ongoing tweets, we have proposed an easily applicable
automated methodology in which the Daily Mean Similarity Scores that show the
similarity between the daily tweet corpus and the target words representing our
area of interest is calculated by using a na\"ive correlation-based technique
without training any Machine Learning Model. The Daily Mean Similarity Scores
have mainly based on cosine similarity and word/sentence embeddings computed by
Multilanguage Universal Sentence Encoder and showed main opinion stream of the
tweets with respect to a certain area of interest, which proves that an ongoing
trend of a specific subject on Twitter can easily be captured in almost real
time by using the proposed methodology in this study. We have also compared the
effectiveness of using word versus sentence embeddings while applying our
methodology and realized that both give almost the same results, whereas using
word embeddings requires less computational time than sentence embeddings, thus
being more effective. This paper will start with an introduction followed by
the background information about the basics, then continue with the explanation
of the proposed methodology and later on finish by interpreting the results and
concluding the findings.
