GTA V

- Grand Theft Auto V had the 2nd highest amount of total reviews

- There 927 total negative reviews gathered (from clusters containing 193+173+164+130+124+31+113 negative reviews)

- There were 243 positive reviews (from clusters containing 54+43+27+22+15+58+24 positive reviews.)

- This gives it a rough ratio of 3.8 negative reviews for every 1 positive review. This was the largest amount in the dataset.

- A large number of complaints revolved around banning. Whether it was a general complaint on bans, unfair banning or the ban appeal.

- A large number of reviews were orientated around general mods references or specific mods. Words that came up were related around “mods” “modding” “openiv”

- Certain missions, vehicles, or purchases were mentioned such as “Royale” & “Alpha”

- There was a less quantity of negative reviews regarding hackers.

- There were a less quantity of negative reviews relating to microtransactions with specific mentions of “rockstar”, “greedy”, “gtx”, “currency”, “pay”, “paying” “overpriced”

Rocket League

- Rocket League had a total of 517 recommended reviews (in clusters of 49+351+117 ) to 10 not-recommended reviews (in clusters of 3+6+1).

- The largest cluster of reviews made specific references to “addictive” “addiction” “fun” as qualities of the gameplay.

- Many positive mentions of the game being “entertaining”, “fun” & “enjoyable”

Rust

- Rust had a distribution of 569 recommended reviews (from clusters of 64+323+182)

- It had a not recommended reviews of 103 not recommend reviews (from clusters of 34+52+17)

- Many references were made mentioning “cancer” with regards to the game itself or the community.

- Other references included “gaming” “gameplay” “ammo” “community” “Gamers” “ultimate” “simulator”

- This game received many mentions of other games including “skyrim”, “fortnight”, “battlefield”, “minecraft”

- Specific items or gameplay mechanics were mentioned like “Death” “health” “dying” “war” “toxic” “survival” “pain” “fire” “build” “nake” “run” “kills” “looting” “gunplay” “spawns”

Overall positive recommended reviews

- 378 recommended reviews skewed towards co-operative video games.

- The most positive and popular recommendations (within two clusters of 351 & 323 reviews) specifically referenced the video games gameplay.

- 129 positive reviews were made making general mentions of the video game as being fun to play (a large portion of these making specific reference to Rocket League)

The negative sentiment reviews gave much meatier insights though.

Overall negative sentiment reviews

- 204 negative reviews that were made in relation to bad game play or poor game play quality.

- In 193 reviews, users complained about being banned by Steam.

- 173 negative reviews were made with reference to “mods”. These reviews were related to mods causing bugs that made their game crash or become unstable.

- 159 users complained about experiencing lag and latency issues within multiplayer video games.

- 153 negative reviews blamed games for crashing and glitching that rendered them unplayable or disruptive.

- In two different clusters that contained 147 & 142 each, negative reviews were posted, voicing complaints regarding hackers, which is common in online multiplayer games.

If I had my time again, I would...

- Like to spend more time mining for insights

- Work on updating or scraping my own dataset with a larger and more updated dataset.

- Track which game developers as well as video games had the highest amount of negative reviews, positive reviews and positive to negative review ratios.

- Begin to visualise each genre of video game, their review totals, and negative to positive review ratios.

- Map out repeatable criticisms or issues with larger marquee games, over a wider & more up to date dataset. (i.e Specific mentions of Fortnite Skins being too expensive, or negative sentiment with reference to GTA V Shark Cards)

End Credits.

Source: https://www.kaggle.com/luthfim/steam-reviews-dataset

Tool for Visualisation and Analysis https://relevance.ai

For those that want to dive deeper or reproduce this experiment:

Broader Clustering results - https://cloud.relevance.ai/dataset/steam_reviews_35k_zerosho...

Game specific clustering results – https://cloud.relevance.ai/dataset/steam_reviews_35k_zerosho...

For those that know Python, how to reproduce: https://drive.google.com/file/d/11EAQN_xYhIBmjU0ItAs5hxvW0Al...

Cleaned dataset source – https://github.com/RelevanceAI/michelangiolo_experiments_rep...