People voice about Lynas Malaysia through Twitter Analysis with R CloudStat:
Lynas was a hot topic, especially Malaysian. This was due to Lynas Malaysia Sdn Bhd (Lynas) will start importing rare earth ore from its Mount Weld mine in Western Australia and process it at the Gebeng Industrial Estate in Pahang, Malaysia.
Rare earth minerals are often found in ores which contain small amounts of radioactive elements such as uranium and thorium. So extracting them from these ores raises a number of health and safety issues. Many members of the public – including residents, non-governmental committees and professional bodies – expressed concern that the Lynas project was not safe, and was a threat to public health and safety.
The determination of government’s to continue implementing this Lynas project caused the anger of Malaysians. And the largest anti-Lynas rally, “Himpunan Hijau 2.0” at Padang MPK 4, Kuantan aimed to pressure government into aborting Lynas project in 26 February 2012.
In this analysis, we will track the tweets from 21 till 28 February 2012. Below, you will get the insights of:
- Lynas’s Total tweets, original tweets, retweets and contributors.
- “Lynas” Tweet counts and trending.
- Tweet behavior over time by users.
- Who is tweeting about Lynas.
- How a person retweets (RT) a message about Lynas.
- Who is retweeting whom’s.
- Top 50 of tweeters, retweet, tweets and created time.
- Sentiment Analysis and Opinion Mining.
The overview of Lynas twitter analysis for past 7 days (21 – 28 Feb).
- Total tweets: 8397
- Original tweets: 6605
- Retweets: 1792
- Contributors: 4305
- Average 2 tweets per contributor
More details tracked daily are the bottom of this analysis.
This chart shows how many tweets had been occurred over past 7 days, reaching the maximum in 25 February, a day before the Anti-Lynas rally.
This plot show how a person tweets and retweets about Lynas.
This is the Word Square of the tweets containing “lynas” as a keyword. The size is varied according to the frequency of the keyword.
This is the sentiment analysis of the tweets. Each tweets will be scanned and matched to our positive and negative word database. The score is calculated using the formula :
Score = # Positive words – # Negative words
The plot Scores over days is as below:
Next is the Top 50 for several categories.