Abstract:
Previous year, many researchers have been
sentiment analysis on many focus languages. They
analyzed and categorizing opinions expressed in a
text. People express their opinions and feeling on
social media as a daily routine. For sentiment
analysis work, data plays an important role. Thus,
social media become interested platform for opinion
mining. On the other hand, low resource languages
face less of sentiment resources (such as sentiment
lexicon, corpus) than English language. It is needed
to overcome language barriers and realize a
sentiment platform capable of scoring in different
languages when global opinion is need to decide
something. In this paper, the expectations and
limitations of machine translation in sentiment
polarity task for Myanmar language is presented. We
experiment with comments of particular news and
general news that are expressed in social media news
pages. Results show that sentiment transfer can be
successful through human translation. This also
demonstrates that translation from Myanmar to
English has a significant eff ect on the preservation
of sentiment by using translation engine. This
happens primarily due to nature of Language but the
results show that machine translation quality plays
the important role in this work.
