Ways of using SV

Search Visualiser is particularly useful for situations where you want to find the key information within a large number of words.

Here are some examples:

Some of these SV features are particularly useful for searching the Internet; some are particularly useful for handling large documents and large sets of documents.

With the SV, you can swiftly see the distributions of your selected keywords throughout a document. That tells you a lot. You can see how often your keywords occur, and where they occur; you can see whether they're scattered evenly across the document, or whether they cluster in just one or two places.

You can also use the interactive hover mode of SV to see the text next to your selected keywords.

The "getting started" basic overview is here

The downloadable PDFs below contain more detailed examples.

New insights into literature

Searching and analysing texts

Background theory:

Finding the most relevant part of a large document

Finding the right document is often just the start – you then need to find the right section. That can take a long time with "find in document" functions on standard browsers. With SV you can swiftly spot the places where your chosen keywords occur together within the document.

Finding what you want among a mass of false positives

A lot of searches produce huge numbers of irrelevant hits. Here's an example. If you're searching for someone by name, then you'll get a lot of irrelevant records about people with the same first name or the same family name.

Searching for a name as a phrase within inverted commas (e.g. "Dr John Smith") reduces the number of irrelevant records, but also filters out a lot of potentially relevant ones, such as records which mention Doctor John Henry Smith or Dr J. Smith. With SV, you can see places where the the words occur close enough together to be promising, and you can easily include synonyms within your search.

Finding something which is not one of the "usual suspects"

A classic problem in online search is eliminating "usual suspects" which don't interest you; it's very difficult to do that without throwing out records which also happen to contain the topic that does interest you. Suppose, for instance, that you're interested in sources of renewable energy other than wind, wave and solar: if you tell your search engine to filter out records containing those three words, then it will also filter out quite a few records which mention not only those three words, but also other terms which are relevant.

You can handle this with SV by looking at the distribution of your keywords within each record. When the keywords occur in bands, with a gap between two bands, this suggests that the text is structured into sections, and that there's a section in the gap which is about something that isn't one of the usual suspects (for instance, a section on tidal power, between a section on wind power and a section on wave power).

Getting a quick overview of a text

With SV, you can quickly see the distribution of keywords across a text. This can tell you a lot about the text; for instance, whether a particular topic is only mentioned in a few places, or whether it recurs repeatedly across the entire text.

Comparing texts to each other

With SV, you can show substantial documents side by side for comparisons, whether for plagiarism checking or for literary criticism or for some other purpose. For instance, you can compare mentions of love and death in two Shakespeare plays; it's possible to show two entire Shakespeare plays on a single screen using the "tiny squares" setting of SV.

Finding relevant records in a foreign language

It's fairly easy to use online software to translate a relevant document out of a foreign language into your own langugage, but how can you decide whether a record is relevant or not in the first place, if it's written in a language that you don't speak?

With SV, you can simply take keywords which you have translated via an online dictionary, and enter them as search terms in the usual way. You can then easily identify the most relevant records (e.g. ones where your keywords frequently occur close to each other) and select them for downloading and translation.