Tweets Analysis - Keyword: @katemakarova1
Overview
Total number of tweets analysed
1
Earliest tweet was on
2023-03-26
Latest tweet was on
2023-03-26
Tweets covering
0 days
Average age of authors' accounts
13 years
Summarization
The tweet suggests that there is a statistical advantage for Vekic in her match against Kvitova, as Kvitova has historically lost in the third round of the Miami Open if she has reached the quarterfinals of the Indian Wells Open prior. This has happened to her in 2013 and 2016, when she lost to Flipkens and Makarova respectively. Additionally, both Flipkens and Makarova went on to play Kvitova at Wimbledon later in the same years, with Flipkens playing her in the quarterfinals in 2013 and Makarova playing her in the second round in 2016.
Topic Modeling
- Statistical analysis of player performance in specific tournaments
- Comparison of player performance in different years
- Player match-ups and historical outcomes
- Prediction of player performance based on past trends and statistics
- Observation and analysis of Grand Slam tournament results
Emotional Analysis
The tweet presents a logical argument and factual evidence in favor of Vekic winning over Kvitova in the upcoming match. There is no specific emotion expressed in the tweet; instead, it relies on statistics and past performances to make a rational prediction.
Trend Analysis
- Statistics and analysis of tennis players' performance in Miami and Indian Wells tournaments
- Mentions of specific tennis players including Petra Kvitova, Donna Vekic, Kirsten Flipkens, and Ekaterina Makarova
- Notable past matches where Kvitova has lost in the third round of Miami after reaching the quarterfinals in Indian Wells
- Potential implications for Kvitova's future performance in Wimbledon based on past results
- Discussion of factors and trends that may impact the outcome of upcoming tennis matches
Types of Tweets
Number of Retweets
0
Percentage of total tweets
0%
Number of Original tweets
0
Percentage of total tweets
0%
Number of tweets that contain Mentions
1
Percentage of total tweets
100%
Number of tweets that were Replies
1
Percentage of total tweets
100%
Number of tweets that were Quotes
0
Percentage of total tweets
0%
Number of tweets that contain Hashtags
0
Percentage of total tweets
0%
Top 5 devices used to tweet
Source | Count |
---|---|
Twitter for Android | 1 |
What devices were used to tweet

Top 10 accounts with highest followers count
Username | Name | Bio | Followers count |
---|---|---|---|
silasofficial_ | silas | /saɪləs/ | Turning Petra Kvitová career stats into really fun facts & advocate of Petra Kvitová's first-week exits at Slams! | 414 |
Top 10 accounts with highest friends count
Username | Name | Bio | Followers count |
---|---|---|---|
silasofficial_ | silas | /saɪləs/ | Turning Petra Kvitová career stats into really fun facts & advocate of Petra Kvitová's first-week exits at Slams! | 907 |
Most active users
Username | Bio | Number of tweets |
---|---|---|
silasofficial_ | /saɪləs/ | Turning Petra Kvitová career stats into really fun facts & advocate of Petra Kvitová's first-week exits at Slams! | 1 |
Tweets per day

Top 10 tweets with highest Retweet count
ID | Text | Retweet count |
---|---|---|
1639886551646887936 | In favour of Vekic is the stat that Kvitova always loses in 3R of Miami if she has reached QF of Indian Wells prior - 2013 (lost to @FlipperKF) and 2016 (lost to @katemakarova1) Also Flipkens and Makarova in same year, each Kvitova at @Wimbledon later - 2013 in QF & 2016 in 2R! | 0 |
Top 10 tweets with highest Like count
ID | Text | Like count |
---|---|---|
1639886551646887936 | In favour of Vekic is the stat that Kvitova always loses in 3R of Miami if she has reached QF of Indian Wells prior - 2013 (lost to @FlipperKF) and 2016 (lost to @katemakarova1) Also Flipkens and Makarova in same year, each Kvitova at @Wimbledon later - 2013 in QF & 2016 in 2R! | 0 |
Top 3 Languages Used In Tweets

Top 10 mentions
Mention | Count |
---|---|
@flipperkf | 1 |
@katemakarova1 | 1 |
@wimbledon | 1 |
Top 10 mentions

Wordcloud of Tweets
